xszheng2020/the_stack_dedup_python_hits_0 · Datasets at Hugging Face

fab80aae07e49b359e0ffb7f6ecbf6e04a0ad735

85,687

py

Python

v12/rhba_utils.py

gavinIRL/RHBotArray

2b537c9657d74e9dd3f9dc8f679080c440df6e0c

[ "MIT" ]

null

v12/rhba_utils.py

gavinIRL/RHBotArray

2b537c9657d74e9dd3f9dc8f679080c440df6e0c

[ "MIT" ]

113

2021-05-02T13:23:10.000Z

2021-07-12T21:22:01.000Z

v12/rhba_utils.py

gavinIRL/RHBotArray

2b537c9657d74e9dd3f9dc8f679080c440df6e0c

[ "MIT" ]

null

import os import cv2 import time import math import ctypes import random import win32ui import win32gui import warnings import win32con import threading import subprocess import pytesseract import numpy as np import pydirectinput from fuzzywuzzy import process from custom_input import CustomInput from win32api import GetSystemMetrics os.chdir(os.path.dirname(os.path.abspath(__file__))) warnings.simplefilter("ignore", DeprecationWarning) class HsvFilter: def __init__(self, hMin=None, sMin=None, vMin=None, hMax=None, sMax=None, vMax=None, sAdd=None, sSub=None, vAdd=None, vSub=None): self.hMin = hMin self.sMin = sMin self.vMin = vMin self.hMax = hMax self.sMax = sMax self.vMax = vMax self.sAdd = sAdd self.sSub = sSub self.vAdd = vAdd self.vSub = vSub class WindowCapture: w = 0 h = 0 hwnd = None cropped_x = 0 cropped_y = 0 offset_x = 0 offset_y = 0 def __init__(self, window_name=None, custom_rect=None): self.custom_rect = custom_rect if window_name is None: self.hwnd = win32gui.GetDesktopWindow() else: self.hwnd = win32gui.FindWindow(None, window_name) if not self.hwnd: raise Exception('Window not found: {}'.format(window_name)) # Declare all the class variables self.w, self.h, self.cropped_x, self.cropped_y self.offset_x, self.offset_y self.update_window_position() def get_screenshot(self): # get the window image data wDC = win32gui.GetWindowDC(self.hwnd) dcObj = win32ui.CreateDCFromHandle(wDC) cDC = dcObj.CreateCompatibleDC() dataBitMap = win32ui.CreateBitmap() dataBitMap.CreateCompatibleBitmap(dcObj, self.w, self.h) cDC.SelectObject(dataBitMap) cDC.BitBlt((0, 0), (self.w, self.h), dcObj, (self.cropped_x, self.cropped_y), win32con.SRCCOPY) # convert the raw data into a format opencv can read signedIntsArray = dataBitMap.GetBitmapBits(True) img = np.fromstring(signedIntsArray, dtype='uint8') img.shape = (self.h, self.w, 4) # free resources dcObj.DeleteDC() cDC.DeleteDC() win32gui.ReleaseDC(self.hwnd, wDC) win32gui.DeleteObject(dataBitMap.GetHandle()) # drop the alpha channel img = img[..., :3] # make image C_CONTIGUOUS img = np.ascontiguousarray(img) return img def focus_window(self): win32gui.SetForegroundWindow(self.hwnd) def update_window_position(self, border=True): self.window_rect = win32gui.GetWindowRect(self.hwnd) self.w = self.window_rect[2] - self.window_rect[0] self.h = self.window_rect[3] - self.window_rect[1] border_pixels = 8 titlebar_pixels = 30 if self.custom_rect is None: if border: self.w = self.w - (border_pixels * 2) self.h = self.h - titlebar_pixels - border_pixels self.cropped_x = border_pixels self.cropped_y = titlebar_pixels else: self.cropped_x = 0 self.cropped_y = 0 self.w += 3 else: self.w = self.custom_rect[2] - self.custom_rect[0] self.h = self.custom_rect[3] - self.custom_rect[1] self.cropped_x = self.custom_rect[0] self.cropped_y = self.custom_rect[1] self.offset_x = self.window_rect[0] + self.cropped_x self.offset_y = self.window_rect[1] + self.cropped_y # WARNING: need to call the update_window_position function to prevent errors # That would come from moving the window after starting the bot def get_screen_position(self, pos): return (pos[0] + self.offset_x, pos[1] + self.offset_y) class BotUtils: def grab_online_servers(): output = subprocess.run("arp -a", capture_output=True).stdout.decode() list_ips = [] with open("servers.txt", "r") as f: lines = f.readlines() for ip in lines: if ip.strip() in output: list_ips.append(ip.strip()) return list_ips def grab_current_lan_ip(): output = subprocess.run( "ipconfig", capture_output=True).stdout.decode() _, output = output.split("IPv4 Address. . . . . . . . . . . : 169") output, _ = output.split("Subnet Mask", maxsplit=1) current_lan_ip = "169" + output.strip() return current_lan_ip def start_server_threads(list_servers): for server in list_servers: t = threading.Thread(target=server.main_loop) t.start() def grab_closest(rel_list: list): closest_index = False smallest_dist = 100000 for i, pair in enumerate(rel_list): x = abs(pair[0]) y = abs(pair[1]) hypot = math.hypot(x, y) if hypot < smallest_dist: smallest_dist = hypot closest_index = i return closest_index def grab_order_closeness(relatives): dists = [] for x, y in relatives: dists.append(math.hypot(x, y)) return sorted(range(len(dists)), key=dists.__getitem__) def grab_order_lowest_y(coords): y_only = [] for _, y in coords: y_only.append(y) return sorted(range(len(y_only)), key=y_only.__getitem__) # Angle is left->right travel of room angle, north being 0deg def move_diagonal(gamename, x, y, angle=90, speed=20, rel=False): # If not a direct relative move command if not rel: if not BotUtils.detect_bigmap_open(gamename): BotUtils.try_toggle_map() player_pos = BotUtils.grab_player_pos(gamename) start_time = time.time() while not player_pos: time.sleep(0.05) if not BotUtils.detect_bigmap_open(gamename): BotUtils.try_toggle_map() time.sleep(0.05) player_pos = BotUtils.grab_player_pos(gamename) if time.time() - start_time > 5: print("Error with finding player") os._exit(1) BotUtils.close_map_and_menu(gamename) relx = player_pos[0] - int(x) rely = int(y) - player_pos[1] while abs(relx) > 100 or abs(rely > 100): CustomInput.press_key(CustomInput.key_map["right"], "right") CustomInput.release_key(CustomInput.key_map["right"], "right") time.sleep(0.02) player_pos = BotUtils.grab_player_pos(gamename) relx = player_pos[0] - int(x) rely = int(y) - player_pos[1] # Otherwise treat x,y as direct commands else: relx = x rely = y mult = 0.707 if relx > 0: keyx = "left" CustomInput.press_key(CustomInput.key_map["left"], "left") timeleftx = float("{:.4f}".format(abs(relx/(speed*mult)))) elif relx < 0: keyx = "right" CustomInput.press_key(CustomInput.key_map["right"], "right") timeleftx = float("{:.4f}".format(abs(relx/(speed*mult)))) else: timeleftx = 0 mult = 1 if rely > 0: keyy = "down" CustomInput.press_key(CustomInput.key_map["down"], "down") timelefty = float("{:.4f}".format(abs(rely/(speed*mult)))) elif rely < 0: keyy = "up" CustomInput.press_key(CustomInput.key_map["up"], "up") timelefty = float("{:.4f}".format(abs(rely/(speed*mult)))) else: timelefty = 0 if relx != 0: timeleftx = float("{:.4f}".format(abs(relx/speed))) first_sleep = min([timeleftx, timelefty]) second_sleep = max([timeleftx, timelefty]) first_key = [keyx, keyy][[timeleftx, timelefty].index(first_sleep)] second_key = [keyx, keyy][[timeleftx, timelefty].index(second_sleep)] if first_sleep < 0.009: if second_sleep < 0.009: pass else: time.sleep(second_sleep-0.009) CustomInput.release_key( CustomInput.key_map[second_key], second_key) elif timelefty == timeleftx: time.sleep(first_sleep-0.009) CustomInput.release_key(CustomInput.key_map[first_key], first_key) CustomInput.release_key( CustomInput.key_map[second_key], second_key) else: time.sleep(first_sleep - 0.009) CustomInput.release_key(CustomInput.key_map[first_key], first_key) time.sleep((second_sleep-first_sleep-0.009)*mult) CustomInput.release_key( CustomInput.key_map[second_key], second_key) def move_towards(value, dir): if dir == "x": if value > 0: key = "left" else: key = "right" elif dir == "y": if value > 0: key = "down" else: key = "up" CustomInput.press_key(CustomInput.key_map[key], key) def move_to(gamename, x, y, angle=90, yfirst=True, speed=22.5, loot=False, plyr=False, rel=False): if not rel: if not BotUtils.detect_bigmap_open(gamename): BotUtils.try_toggle_map() player_pos = BotUtils.grab_player_pos(gamename) start_time = time.time() while not player_pos: time.sleep(0.05) if not BotUtils.detect_bigmap_open(gamename): BotUtils.try_toggle_map() time.sleep(0.05) player_pos = BotUtils.grab_player_pos(gamename) if time.time() - start_time > 5: print("Error with finding player") os._exit(1) BotUtils.close_map_and_menu(gamename) relx = player_pos[0] - int(x) rely = int(y) - player_pos[1] while abs(relx) > 100 or abs(rely > 100): CustomInput.press_key(CustomInput.key_map["right"], "right") CustomInput.release_key(CustomInput.key_map["right"], "right") time.sleep(0.02) player_pos = BotUtils.grab_player_pos(gamename) relx = player_pos[0] - int(x) rely = int(y) - player_pos[1] else: relx = x rely = y if not yfirst: if not loot: BotUtils.resolve_dir_v2(relx, "x", speed) BotUtils.resolve_dir_v2(rely, "y", speed) else: lootfound = BotUtils.resolve_dir_with_looting( relx, "x", speed, gamename) if lootfound: Looting.grab_all_visible_loot(gamename, plyr) # Continue to destination without further looting (prevent stuck) BotUtils.move_to(gamename, x, y, angle, yfirst, speed) # When at destination check for loot again if Looting.check_for_loot(gamename): Looting.grab_all_visible_loot(gamename, plyr) # If needs be return to destination BotUtils.move_to(gamename, x, y, angle, yfirst, speed) else: lootfound = BotUtils.resolve_dir_with_looting( rely, "y", speed, gamename) if lootfound: Looting.grab_all_visible_loot(gamename, plyr) # Continue to destination without further looting (prevent stuck) BotUtils.move_to(gamename, x, y, angle, yfirst, speed) # When at destination check for loot again if Looting.check_for_loot(gamename): Looting.grab_all_visible_loot(gamename, plyr) # If needs be return to destination BotUtils.move_to( gamename, x, y, angle, yfirst, speed) else: if not loot: BotUtils.resolve_dir_v2(rely, "y", speed) BotUtils.resolve_dir_v2(relx, "x", speed) else: lootfound = BotUtils.resolve_dir_with_looting( rely, "y", speed, gamename) if lootfound: Looting.grab_all_visible_loot(gamename, plyr) # Continue to destination without further looting (prevent stuck) BotUtils.move_to(gamename, x, y, angle, yfirst, speed) # When at destination check for loot again if Looting.check_for_loot(gamename): Looting.grab_all_visible_loot(gamename, plyr) # If needs be return to destination BotUtils.move_to(gamename, x, y, angle, yfirst, speed) else: lootfound = BotUtils.resolve_dir_with_looting( relx, "x", speed, gamename) if lootfound: Looting.grab_all_visible_loot(gamename, plyr) # Continue to destination without further looting (prevent stuck) BotUtils.move_to(gamename, x, y, angle, yfirst, speed) # When at destination check for loot again if Looting.check_for_loot(gamename): Looting.grab_all_visible_loot(gamename, plyr) # If needs be return to destination BotUtils.move_to( gamename, x, y, angle, yfirst, speed) def resolve_dir_v2(value, dir, speed): if dir == "x": if value > 0: key = "left" else: key = "right" elif dir == "y": if value > 0: key = "down" else: key = "up" time_reqd = abs(value/speed) if time_reqd > 0.003: CustomInput.press_key(CustomInput.key_map[key], key) time.sleep(time_reqd-0.003) CustomInput.release_key(CustomInput.key_map[key], key) def resolve_dir_with_looting(value, dir, speed, gamename): if dir == "x": if value > 0: key = "left" else: key = "right" elif dir == "y": if value > 0: key = "down" else: key = "up" time_reqd = abs(value/speed) start_time = time.time() if time_reqd > 0.003: CustomInput.press_key(CustomInput.key_map[key], key) # Maximum lootcheck time is about 0.3secs worst case # Nominal is about 0.2s if time_reqd < 2: time.sleep(time_reqd-0.003) CustomInput.release_key(CustomInput.key_map[key], key) else: BotUtils.close_map(gamename) loops = math.floor(time_reqd/2) for i in range(loops): time.sleep(1.65) result = Looting.check_for_loot(gamename) if result: CustomInput.release_key(CustomInput.key_map[key], key) return True time_left = start_time+time_reqd-time.time() time.sleep(time_left) CustomInput.release_key(CustomInput.key_map[key], key) return Looting.check_for_loot(gamename) def resolve_single_direction(speed, value, dir, PAG=False): if not PAG: sleep_time = 0.003 else: sleep_time = 0.1 if dir == "x": if value > 0: key = "left" else: key = "right" elif dir == "y": if value > 0: key = "down" else: key = "up" time_reqd = abs(value/speed) key_map = CustomInput.grab_key_dict() if not PAG: CustomInput.press_key(key_map[key], key) else: pydirectinput.keyDown(key) try: time.sleep(time_reqd-sleep_time) except: pass if not PAG: CustomInput.release_key(key_map[key], key) else: pydirectinput.keyDown(key) def list_window_names(): def winEnumHandler(hwnd, ctx): if win32gui.IsWindowVisible(hwnd): print(hex(hwnd), win32gui.GetWindowText(hwnd)) win32gui.EnumWindows(winEnumHandler, None) def grab_hpbar_locations(gamename=False): if gamename: wincap = WindowCapture(gamename, [100, 135, 1223, 688]) original_image = wincap.get_screenshot() else: original_image = cv2.imread(os.path.dirname( os.path.abspath(__file__)) + "/testimages/healthbars.jpg") filter = HsvFilter(20, 174, 245, 26, 193, 255, 0, 0, 0, 0) output_image = BotUtils.filter_blackwhite_invert( filter, original_image, True) output_image = cv2.blur(output_image, (2, 2)) _, thresh = cv2.threshold(output_image, 127, 255, 0) contours, _ = cv2.findContours( thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) contours = sorted(contours, key=cv2.contourArea, reverse=True) if len(contours) < 2: return False contours.pop(0) rectangles = [] for contour in contours: (x, y), _ = cv2.minEnclosingCircle(contour) rectangles.append([x-10, y, 20, 5]) rectangles.append([x-10, y, 20, 5]) rectangles, _ = cv2.groupRectangles( rectangles, groupThreshold=1, eps=0.8) points = [] for (x, y, w, h) in rectangles: center_x = x + int(w/2) center_y = y + int(h/2) points.append((center_x, center_y)) return points def grab_character_location(player_name, gamename=False): player_chars = "".join(set(player_name)) if gamename: wincap = WindowCapture(gamename, [200, 235, 1123, 688]) original_image = wincap.get_screenshot() else: original_image = cv2.imread(os.path.dirname( os.path.abspath(__file__)) + "/testimages/test_sensitive.jpg") filter = HsvFilter(0, 0, 119, 179, 49, 255, 0, 0, 0, 0) output_image = BotUtils.filter_blackwhite_invert( filter, original_image, return_gray=True) rgb = cv2.cvtColor(output_image, cv2.COLOR_GRAY2RGB) tess_config = '--psm 6 --oem 3 -c tessedit_char_whitelist=' + player_chars results = pytesseract.image_to_data( rgb, output_type=pytesseract.Output.DICT, lang='eng', config=tess_config) try: best_match, _ = process.extractOne( player_name, results["text"], score_cutoff=0.8) i = results["text"].index(best_match) x = int(results["left"][i] + (results["width"][i]/2)) y = int(results["top"][i] + (results["height"][i]/2)) # Account for the rect x += 200 y += 235 return x, y except: return 640, 382 def shift_channel(c, amount): if amount > 0: lim = 255 - amount c[c >= lim] = 255 c[c < lim] += amount elif amount < 0: amount = -amount lim = amount c[c <= lim] = 0 c[c > lim] -= amount return c def filter_blackwhite_invert(filter: HsvFilter, existing_image, return_gray=False, threshold=67, max=255): hsv = cv2.cvtColor(existing_image, cv2.COLOR_BGR2HSV) hsv_filter = filter # add/subtract saturation and value h, s, v = cv2.split(hsv) s = BotUtils.shift_channel(s, hsv_filter.sAdd) s = BotUtils.shift_channel(s, -hsv_filter.sSub) v = BotUtils.shift_channel(v, hsv_filter.vAdd) v = BotUtils.shift_channel(v, -hsv_filter.vSub) hsv = cv2.merge([h, s, v]) # Set minimum and maximum HSV values to display lower = np.array([hsv_filter.hMin, hsv_filter.sMin, hsv_filter.vMin]) upper = np.array([hsv_filter.hMax, hsv_filter.sMax, hsv_filter.vMax]) # Apply the thresholds mask = cv2.inRange(hsv, lower, upper) result = cv2.bitwise_and(hsv, hsv, mask=mask) # convert back to BGR img = cv2.cvtColor(result, cv2.COLOR_HSV2BGR) # now change it to greyscale grayImage = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # now change it to black and white (thresh, blackAndWhiteImage) = cv2.threshold( grayImage, threshold, max, cv2.THRESH_BINARY) # now invert it inverted = (255-blackAndWhiteImage) if return_gray: return inverted inverted = cv2.cvtColor(inverted, cv2.COLOR_GRAY2BGR) return inverted def convert_pynput_to_pag(button): PYNPUT_SPECIAL_CASE_MAP = { 'alt_l': 'altleft', 'alt_r': 'altright', 'alt_gr': 'altright', 'caps_lock': 'capslock', 'ctrl_l': 'ctrlleft', 'ctrl_r': 'ctrlright', 'page_down': 'pagedown', 'page_up': 'pageup', 'shift_l': 'shiftleft', 'shift_r': 'shiftright', 'num_lock': 'numlock', 'print_screen': 'printscreen', 'scroll_lock': 'scrolllock', } # example: 'Key.F9' should return 'F9', 'w' should return as 'w' cleaned_key = button.replace('Key.', '') if cleaned_key in PYNPUT_SPECIAL_CASE_MAP: return PYNPUT_SPECIAL_CASE_MAP[cleaned_key] return cleaned_key def detect_player_name(gamename): plyrname_rect = [165, 45, 320, 65] plyrname_wincap = WindowCapture(gamename, plyrname_rect) plyrname_filt = HsvFilter(0, 0, 103, 89, 104, 255, 0, 0, 0, 0) # get an updated image of the game image = plyrname_wincap.get_screenshot() # pre-process the image image = BotUtils.apply_hsv_filter( image, plyrname_filt) rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) results = pytesseract.image_to_data( rgb, output_type=pytesseract.Output.DICT, lang='eng') biggest = 0 name = False for entry in results["text"]: if len(entry) > biggest: name = entry biggest = len(entry) return name def detect_level_name(gamename): wincap = WindowCapture(gamename, [1121, 31, 1248, 44]) existing_image = wincap.get_screenshot() filter = HsvFilter(0, 0, 0, 169, 34, 255, 0, 0, 0, 0) save_image = BotUtils.apply_hsv_filter(existing_image, filter) gray_image = cv2.cvtColor(save_image, cv2.COLOR_BGR2GRAY) (thresh, blackAndWhiteImage) = cv2.threshold( gray_image, 129, 255, cv2.THRESH_BINARY) # now invert it inverted = (255-blackAndWhiteImage) save_image = cv2.cvtColor(inverted, cv2.COLOR_GRAY2BGR) rgb = cv2.cvtColor(save_image, cv2.COLOR_BGR2RGB) tess_config = '--psm 7 --oem 3 -c tessedit_char_whitelist=01234567890ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz' result = pytesseract.image_to_string( rgb, lang='eng', config=tess_config)[:-2] return result def apply_hsv_filter(original_image, hsv_filter: HsvFilter): # convert image to HSV hsv = cv2.cvtColor(original_image, cv2.COLOR_BGR2HSV) # add/subtract saturation and value h, s, v = cv2.split(hsv) s = BotUtils.shift_channel(s, hsv_filter.sAdd) s = BotUtils.shift_channel(s, -hsv_filter.sSub) v = BotUtils.shift_channel(v, hsv_filter.vAdd) v = BotUtils.shift_channel(v, -hsv_filter.vSub) hsv = cv2.merge([h, s, v]) # Set minimum and maximum HSV values to display lower = np.array([hsv_filter.hMin, hsv_filter.sMin, hsv_filter.vMin]) upper = np.array([hsv_filter.hMax, hsv_filter.sMax, hsv_filter.vMax]) # Apply the thresholds mask = cv2.inRange(hsv, lower, upper) result = cv2.bitwise_and(hsv, hsv, mask=mask) # convert back to BGR for imshow() to display it properly img = cv2.cvtColor(result, cv2.COLOR_HSV2BGR) return img def detect_sect_clear(gamename=False): if not gamename: with open("gamename.txt") as f: gamename = f.readline() wincap = WindowCapture(gamename, custom_rect=[ 464+156, 640, 464+261, 641]) image = wincap.get_screenshot() a, b, c = [int(i) for i in image[0][0]] d, e, f = [int(i) for i in image[0][-1]] if a+b+c > 700: if d+e+f > 700: return True return False def detect_boss_healthbar(gamename=False): if not gamename: with open("gamename.txt") as f: gamename = f.readline() wincap = WindowCapture(gamename, custom_rect=[ 415+97, 105+533, 415+98, 105+534]) image = wincap.get_screenshot() # bgr a, b, c = [int(i) for i in image[0][0]] d, e, f = [int(i) for i in image[0][-1]] if c+f > 440: if a+b+d+e < 80: return True return False def detect_xprompt(gamename=False): if not gamename: with open("gamename.txt") as f: gamename = f.readline() wincap = WindowCapture(gamename, custom_rect=[ 1137, 694, 1163, 695]) image = wincap.get_screenshot() a, b, c = [int(i) for i in image[0][0]] d, e, f = [int(i) for i in image[0][-1]] if a+b+d+e > 960 and c+f == 140: return True else: return False def grab_player_pos(gamename=False, map_rect=None, rect_rel=False): if not gamename: with open("gamename.txt") as f: gamename = f.readline() if not map_rect: wincap = WindowCapture(gamename, [561, 282, 1111, 666]) else: wincap = WindowCapture(gamename, map_rect) filter = HsvFilter(34, 160, 122, 50, 255, 255, 0, 0, 0, 0) image = wincap.get_screenshot() save_image = BotUtils.filter_blackwhite_invert(filter, image) vision = Vision('plyr.jpg') rectangles = vision.find( save_image, threshold=0.31, epsilon=0.5) if len(rectangles) < 1: return False, False points = vision.get_click_points(rectangles) x, y = points[0] if not map_rect: x += 561 y += 282 return x, y elif rect_rel: x += map_rect[0] y += map_rect[1] return x, y else: x += wincap.window_rect[0] y += wincap.window_rect[1] return x, y def grab_level_rects(): rects = {} # Load the translation from name to num with open("lvl_name_num.txt") as f: num_names = f.readlines() for i, entry in enumerate(num_names): num_names[i] = entry.split("-") # Load the num to rect catalogue with open("catalogue.txt") as f: nums_rects = f.readlines() for i, entry in enumerate(nums_rects): nums_rects[i] = entry.split("-") # Then add each rect to the rects dict against name for number, name in num_names: for num, area, rect in nums_rects: if area == "FM" and num == number: rects[name.rstrip().replace(" ", "")] = rect.rstrip() if "1" in name: rects[name.rstrip().replace( " ", "").replace("1", "L")] = rect.rstrip() if "ri" in name: rects[name.rstrip().replace( " ", "").replace("ri", "n").replace("1", "L")] = rect.rstrip() break return rects def grab_level_rects_and_speeds(): rects = {} speeds = {} # Load the translation from name to num with open("lvl_name_num.txt") as f: num_names = f.readlines() for i, entry in enumerate(num_names): num_names[i] = entry.split("-") # Load the num to rect catalogue with open("catalogue.txt") as f: nums_rects = f.readlines() for i, entry in enumerate(nums_rects): nums_rects[i] = entry.split("-") # Finally load the level speeds with open("lvl_speed.txt") as f: num_speeds = f.readlines() for i, entry in enumerate(num_speeds): num_speeds[i] = entry.split("|") # Then add each rect to the rects dict against name # Also add each speed to the speed dict against name for number, name in num_names: for num, area, rect in nums_rects: if area == "FM" and num == number: rects[name.rstrip().replace(" ", "")] = rect.rstrip() if "1" in name: rects[name.rstrip().replace( " ", "").replace("1", "L")] = rect.rstrip() if "ri" in name: rects[name.rstrip().replace( " ", "").replace("ri", "n").replace("1", "L")] = rect.rstrip() break for num, speed in num_speeds: if num == number: speeds[name.rstrip().replace( " ", "")] = float(speed.rstrip()) if "1" in name: speeds[name.rstrip().replace( " ", "").replace("1", "L")] = float(speed.rstrip()) if "ri" in name: speeds[name.rstrip().replace( " ", "").replace("ri", "n").replace("1", "L")] = float(speed.rstrip()) break return rects, speeds def string_to_rect(string: str): # This converts the rect from catalogue into int list return [int(i) for i in string.split(',')] def move_mouse_centre(gamename=False): if not gamename: with open("gamename.txt") as f: gamename = f.readline() wincap = WindowCapture(gamename) centre_x = int(0.5 * wincap.w + wincap.window_rect[0]) centre_y = int(0.5 * wincap.h + wincap.window_rect[1]) ctypes.windll.user32.SetCursorPos(centre_x, centre_y) def detect_bigmap_open(gamename=False): if not gamename: with open("gamename.txt") as f: gamename = f.readline() wincap = WindowCapture(gamename, custom_rect=[819, 263, 855, 264]) image = wincap.get_screenshot() a, b, c = [int(i) for i in image[0][0]] d, e, f = [int(i) for i in image[0][-2]] if a+b+c < 30: if d+e+f > 700: return True return False def detect_menu_open(gamename=False): if not gamename: with open("gamename.txt") as f: gamename = f.readline() wincap = WindowCapture(gamename, custom_rect=[595, 278, 621, 281]) image = wincap.get_screenshot() a, b, c = [int(i) for i in image[0][0]] d, e, f = [int(i) for i in image[0][-1]] if a+b+c > 700: if d+e+f > 700: return True return False def convert_list_to_rel(item_list, playerx, playery, yoffset=0): return_list = [] for item in item_list: relx = playerx - item[0] rely = item[1] - playery - yoffset return_list.append((relx, rely)) return return_list def close_map_and_menu(gamename=False): if not gamename: with open("gamename.txt") as f: gamename = f.readline() game_wincap = WindowCapture(gamename) if BotUtils.detect_menu_open(gamename): BotUtils.close_esc_menu(game_wincap) if BotUtils.detect_bigmap_open(gamename): BotUtils.close_map(game_wincap) def try_toggle_map(): pydirectinput.keyDown("m") time.sleep(0.05) pydirectinput.keyUp("m") time.sleep(0.08) def try_toggle_map_clicking(gamename=False): if not gamename: with open("gamename.txt") as f: gamename = f.readline() game_wincap = WindowCapture(gamename) pydirectinput.click( int(1262+game_wincap.window_rect[0]), int(64+game_wincap.window_rect[1])) def close_map(game_wincap=False): if not game_wincap: with open("gamename.txt") as f: gamename = f.readline() game_wincap = WindowCapture(gamename) pydirectinput.click( int(859+game_wincap.window_rect[0]), int(260+game_wincap.window_rect[1])) def close_esc_menu(game_wincap=False): if not game_wincap: with open("gamename.txt") as f: gamename = f.readline() game_wincap = WindowCapture(gamename) pydirectinput.click( int(749+game_wincap.window_rect[0]), int(280+game_wincap.window_rect[1])) def get_monitor_scaling(): scaleFactor = ctypes.windll.shcore.GetScaleFactorForDevice(0) / 100 return float(scaleFactor) def grab_res_scroll_left(gamename): wincap = WindowCapture(gamename, [112, 130, 125, 143]) image = wincap.get_screenshot() filter = HsvFilter(0, 0, 0, 179, 18, 255, 0, 0, 0, 0) image = BotUtils.apply_hsv_filter(image, filter) rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) tess_config = '--psm 7 --oem 3 -c tessedit_char_whitelist=1234567890' result = pytesseract.image_to_string( rgb, lang='eng', config=tess_config)[:-2] return int(result) def read_mission_name(gamename): wincap = WindowCapture(gamename, [749, 152, 978, 170]) image = wincap.get_screenshot() rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) tess_config = '--psm 7 --oem 3 -c tessedit_char_whitelist=ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz' result = pytesseract.image_to_string( rgb, lang='eng', config=tess_config)[:-2] return result def convert_click_to_ratio(gamename, truex, truey): wincap = WindowCapture(gamename) wincap.update_window_position(border=False) scaling = BotUtils.get_monitor_scaling() # print(scaling) relx = (truex - (wincap.window_rect[0] * scaling)) rely = (truey - (wincap.window_rect[1] * scaling)) # print("relx, rely, w, h: {},{},{},{}".format( # relx, rely, wincap.w, wincap.h)) ratx = relx/(wincap.w * scaling) raty = rely/(wincap.h * scaling) return ratx, raty def convert_ratio_to_click(ratx, raty, gamename=False): if not gamename: with open("gamename.txt") as f: gamename = f.readline() wincap = WindowCapture(gamename) relx = int(ratx * wincap.w) rely = int(raty * wincap.h) truex = int((relx + wincap.window_rect[0])) truey = int((rely + wincap.window_rect[1])) return truex, truey def convert_true_to_window(gamename, truex, truey): scaling = BotUtils.get_monitor_scaling() wincap = WindowCapture(gamename) relx = (truex/scaling) - wincap.window_rect[0] rely = (truey/scaling) - wincap.window_rect[1] return relx, rely def convert_window_to_true(gamename, relx, rely): wincap = WindowCapture(gamename) truex = int(relx + wincap.window_rect[0]) truey = int(rely + wincap.window_rect[1]) return truex, truey def find_other_player(gamename, all=False): othr_plyr_vision = Vision("otherplayerinvert.jpg") othr_plyr_wincap = WindowCapture(gamename, [1100, 50, 1260, 210]) image = othr_plyr_wincap.get_screenshot() filter = HsvFilter(24, 194, 205, 31, 255, 255, 0, 0, 0, 0) image = cv2.blur(image, (4, 4)) image = BotUtils.filter_blackwhite_invert(filter, image) rectangles = othr_plyr_vision.find( image, threshold=0.61, epsilon=0.5) points = othr_plyr_vision.get_click_points(rectangles) if len(points) >= 1: if not all: relx = points[0][0] - 0 rely = 0 - points[0][1] return relx, rely else: return points return False def find_enemy(gamename, all=False): othr_plyr_vision = Vision("otherplayerinvert.jpg") othr_plyr_wincap = WindowCapture(gamename, [1100, 50, 1260, 210]) image = othr_plyr_wincap.get_screenshot() filter = HsvFilter(0, 198, 141, 8, 255, 255, 0, 0, 0, 0) image = cv2.blur(image, (4, 4)) image = BotUtils.filter_blackwhite_invert(filter, image) rectangles = othr_plyr_vision.find( image, threshold=0.41, epsilon=0.5) points = othr_plyr_vision.get_click_points(rectangles) if len(points) >= 1: if not all: relx = points[0][0] - 0 rely = 0 - points[0][1] return relx, rely else: return points return False def find_midlevel_event(gamename=False, playerx=False, playery=False): if not gamename: with open("gamename.txt") as f: gamename = f.readline() if not playerx: playerx, playery = BotUtils.grab_player_pos( gamename, [1100, 50, 1260, 210], True) filter = HsvFilter(76, 247, 170, 100, 255, 255, 0, 0, 0, 0) vision = Vision("otherplayerinvert.jpg") wincap = WindowCapture(gamename, [1100, 50, 1260, 210]) image = wincap.get_screenshot() image = cv2.blur(image, (4, 4)) image = BotUtils.filter_blackwhite_invert(filter, image) rectangles = vision.find( image, threshold=0.61, epsilon=0.5) points = vision.get_click_points(rectangles) if len(points) >= 1: relx = points[0][0] - playerx rely = playery - points[0][1] return relx, rely return False, False def stop_movement(follower=False): if follower: follower.pressed_keys = [] for key in ["up", "down", "left", "right"]: CustomInput.release_key(CustomInput.key_map[key], key) class Looting: def loot_current_room(gamename, player_name, search_points=False): # Start by picking up loot already in range BotUtils.close_map_and_menu(gamename) Looting.grab_nearby_loot(gamename) # Then try grabbing all visible far loot Looting.grab_all_visible_loot(gamename, player_name) # Then once that is exhausted cycle through the searchpoints if search_points: for point in search_points: x, y, first_dir = point BotUtils.move_to(gamename, x, y, yfirst=first_dir == "y") Looting.grab_nearby_loot(gamename) BotUtils.close_map_and_menu(gamename) Looting.grab_all_visible_loot(gamename, player_name) def grab_nearby_loot(gamename): count = 0 while BotUtils.detect_xprompt(gamename): if count > 12: break pydirectinput.press("x") count += 1 time.sleep(0.09) CustomInput.press_key(CustomInput.key_map["right"], "right") CustomInput.release_key(CustomInput.key_map["right"], "right") def grab_all_visible_loot(gamename, player_name): start_time = time.time() while True: if time.time() - start_time > 20: break outcome = Looting.try_find_and_grab_loot( gamename, player_name) if outcome == "noloot": break elif outcome == "noplayer": pydirectinput.press("right") outcome = Looting.try_find_and_grab_loot( gamename, player_name) if outcome == "noplayer": break elif outcome == "falsepos": break elif outcome == True: count = 0 while BotUtils.detect_xprompt(gamename): if count > 12: break pydirectinput.press("x") count += 1 time.sleep(0.09) def check_for_loot(gamename): # This will be a lightweight check for any positive loot ident # Meant to be used when moving and normal looting has ceased # i.e. opportunistic looting data = Looting.grab_farloot_locations( gamename, return_image=True) if not data: return False else: loot_list, image, xoff, yoff = data confirmed = False try: for _, coords in enumerate(loot_list): x, y = coords x -= xoff y -= yoff rgb = image[y-22:y+22, x-75:x+75] filter = HsvFilter(0, 0, 131, 151, 255, 255, 0, 0, 0, 0) rgb = BotUtils.apply_hsv_filter(rgb, filter) tess_config = '--psm 7 --oem 3 -c tessedit_char_whitelist=ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz' result = pytesseract.image_to_string( rgb, lang='eng', config=tess_config)[:-2] if len(result) > 3: return True except: return False if not confirmed: return False def try_find_and_grab_loot(gamename, player_name, loot_lowest=True, printout=False): # First need to close anything that might be in the way BotUtils.close_map_and_menu(gamename) # Then grab loot locations loot_list = Looting.grab_farloot_locations(gamename) if not loot_list: # print("No loot found") return "noloot" # else: # print("Loot found") playerx, playery = BotUtils.grab_character_location( player_name, gamename) # If didn't find player then try once more if not playerx: playerx, playery = BotUtils.grab_character_location( player_name, gamename) if not playerx: return "noplayer" # if want to always loot the nearest first despite the cpu hit if not loot_lowest: # Then convert lootlist to rel_pos list relatives = BotUtils.convert_list_to_rel( loot_list, playerx, playery, 275) # Grab the indexes in ascending order of closesness order = BotUtils.grab_order_closeness(relatives) # Then reorder the lootlist to match loot_list = [x for _, x in sorted(zip(order, loot_list))] # Otherwise if want to loot from bottom of screen to top # Typically better as see all loot then in y direction # but potentially miss loot in x direction else: # Grab the indexes in ascending order of distance from # bottom of the screen order = BotUtils.grab_order_lowest_y(loot_list) # Then reorder the lootlist to match loot_list = [x for _, x in sorted(zip(order, loot_list))] # print(len(loot_list)) confirmed = False for index, coords in enumerate(loot_list): x, y = coords wincap = WindowCapture(gamename, [x-95, y-50, x+95, y+50]) rgb = wincap.get_screenshot() filter = HsvFilter(0, 0, 131, 151, 255, 255, 0, 0, 0, 0) rgb = BotUtils.apply_hsv_filter(rgb, filter) # cv2.imwrite("testytest.jpg", rgb) tess_config = '--psm 5 --oem 3 -c tessedit_char_whitelist=ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz' result = pytesseract.image_to_string( rgb, lang='eng', config=tess_config)[:-2] if len(result) > 3: if printout: print(result) confirmed = loot_list[index] break if not confirmed: # print("Lootname not confirmed or detected") return "noloot" relx = playerx - confirmed[0] rely = confirmed[1] - playery - 275 rect = [confirmed[0]-100, confirmed[1] - 30, confirmed[0]+100, confirmed[1]+30] BotUtils.move_towards(relx, "x") loop_time = time.time() time_remaining = 0.1 time.sleep(0.01) while time_remaining > 0: time.sleep(0.003) if BotUtils.detect_xprompt(gamename): break try: newx, newy = Looting.grab_farloot_locations(gamename, rect)[ 0] time_taken = time.time() - loop_time movementx = confirmed[0] - newx speed = movementx/time_taken if speed != 0: time_remaining = abs( relx/speed) - time_taken rect = [newx-100, newy-30, newx+100, newy+30] except: try: time.sleep(time_remaining) break except: return False for key in ["left", "right"]: CustomInput.release_key(CustomInput.key_map[key], key) BotUtils.move_towards(rely, "y") start_time = time.time() if rely < 0: expected_time = abs(rely/7.5) else: expected_time = abs(rely/5.5) while not BotUtils.detect_xprompt(gamename): time.sleep(0.005) # After moving in opposite direction if time.time() - start_time > 10: # If have moved opposite with no result for equal amount if time.time() - start_time > 10 + 2*(1 + expected_time): for key in ["up", "down"]: CustomInput.release_key(CustomInput.key_map[key], key) # Return falsepos so that it will ignore this detection return "falsepos" # If no result for 3 seconds elif time.time() - start_time > 1 + expected_time: # Try moving in the opposite direction for key in ["up", "down"]: CustomInput.release_key(CustomInput.key_map[key], key) BotUtils.move_towards(-1*rely, "y") start_time -= 8.5 for key in ["up", "down"]: CustomInput.release_key(CustomInput.key_map[key], key) pydirectinput.press("x") return True def grab_farloot_locations(gamename=False, rect=False, return_image=False): if gamename: if not rect: rect1 = [100, 160, 1223, 688] wincap = WindowCapture(gamename, rect1) else: wincap = WindowCapture(gamename, rect) original_image = wincap.get_screenshot() else: original_image = cv2.imread(os.path.dirname( os.path.abspath(__file__)) + "/testimages/lootscene.jpg") filter = HsvFilter(15, 180, 0, 20, 255, 63, 0, 0, 0, 0) output_image = BotUtils.filter_blackwhite_invert( filter, original_image, True, 0, 180) output_image = cv2.blur(output_image, (8, 1)) output_image = cv2.blur(output_image, (8, 1)) output_image = cv2.blur(output_image, (8, 1)) _, thresh = cv2.threshold(output_image, 127, 255, 0) contours, _ = cv2.findContours( thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) contours = sorted(contours, key=cv2.contourArea, reverse=True) if len(contours) < 2: return False contours.pop(0) rectangles = [] for contour in contours: (x, y), _ = cv2.minEnclosingCircle(contour) rectangles.append([x-50, y, 100, 5]) rectangles.append([x-50, y, 100, 5]) rectangles, _ = cv2.groupRectangles( rectangles, groupThreshold=1, eps=0.9) if len(rectangles) < 1: return False points = [] for (x, y, w, h) in rectangles: # Account for the rect if rect: # Account for the rect x += rect[0] y += rect[1] else: x += 100 y += 135 center_x = x + int(w/2) center_y = y + int(h/2) points.append((center_x, center_y)) if return_image: if rect: return points, original_image, rect[0], rect[1] else: return points, original_image, rect1[0], rect1[1] return points class Events: def choose_random_reward(gamename): wincap = WindowCapture(gamename) posx = wincap.window_rect[0] + (460+(180*random.randint(0, 2))) posy = wincap.window_rect[1] + (200+(132*random.randint(0, 3))) pydirectinput.click(int(posx), int(posy)) time.sleep(0.1) # Now accept the reward pydirectinput.click( wincap.window_rect[0]+750, wincap.window_rect[1]+720) def detect_reward_choice_open(gamename): wincap = WindowCapture(gamename, [503, 90, 535, 92]) image = wincap.get_screenshot() a, b, c = [int(i) for i in image[0][0]] d, e, f = [int(i) for i in image[0][-1]] if a + d > 400: if b + e > 500: if c + f < 105: return True return False def detect_move_reward_screen(gamename): wincap = WindowCapture(gamename, [581, 270, 593, 272]) image = wincap.get_screenshot() a, b, c = [int(i) for i in image[0][0]] d, e, f = [int(i) for i in image[0][-1]] if a + d > 360 and a + d < 400: if b + e > 360 and b + e < 400: if c + f < 10: return True return False def detect_endlevel_chest(gamename): wincap = WindowCapture(gamename, [454, 250, 525, 252]) image = wincap.get_screenshot() a, b, c = [int(i) for i in image[0][0]] d, e, f = [int(i) for i in image[0][-1]] if a + d < 50: if b + e > 480: if c + f > 290 and c+f < 320: return True return False def detect_endlevel_bonus_area(gamename): wincap = WindowCapture(gamename, [503, 487, 514, 589]) image = wincap.get_screenshot() a, b, c = [int(i) for i in image[0][0]] d, e, f = [int(i) for i in image[0][-1]] if a + d > 400: if b + e > 400: if c + f > 400: return True return False def detect_in_dungeon(wincap=False): if not wincap: with open("gamename.txt") as f: gamename = f.readline() wincap = WindowCapture(gamename, [1090, 331, 1092, 353]) image = wincap.get_screenshot() a, b, c = [int(i) for i in image[0][0]] d, e, f = [int(i) for i in image[-1][0]] if d < 20: if a + b + e > 400 and a+b+e < 500: if c + f > 480: return True return False def detect_go(gamename): wincap = WindowCapture(gamename, [623, 247, 628, 249]) image = wincap.get_screenshot() a, b, c = [int(i) for i in image[0][0]] if a < 30: if b > 240: if c > 140: return True return False def detect_one_card(gamename): # Cards only show up once one has been picked # Therefore need to check against bronze, gold, silver wincap = WindowCapture(gamename, [833, 44, 835, 46]) image = wincap.get_screenshot() a, b, c = [int(i) for i in image[0][0]] # Bronze if a == 27: if b == 48: if c == 87: return True # Silver if a == 139: if b == 139: if c == 139: return True # Gold if a == 38: if b == 129: if c == 160: return True return False def detect_yes_no(gamename): wincap = WindowCapture(gamename, [516, 426, 541, 441]) image = wincap.get_screenshot() rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) tess_config = '--psm 7 --oem 3 -c tessedit_char_whitelist=Yes' result = pytesseract.image_to_string( rgb, lang='eng', config=tess_config)[:-2] if result == "Yes": return True return False def detect_resurrect_prompt(gamename): wincap = WindowCapture(gamename, [763, 490, 818, 492]) image = wincap.get_screenshot() a, b, c = [int(i) for i in image[0][0]] d, e, f = [int(i) for i in image[-1][0]] if a + d > 500: if b + e > 500: if c + f > 500: return True return False def detect_store(gamename=False): if not gamename: with open("gamename.txt") as f: gamename = f.readline() wincap = WindowCapture(gamename, [1084, 265, 1099, 267]) image = wincap.get_screenshot() a, b, c = [int(i) for i in image[0][0]] d, e, f = [int(i) for i in image[-1][0]] if a + d > 500: if b + e > 500: if c + f > 500: return True return False class RHClick: def click_yes(gamename): wincap = WindowCapture(gamename) pydirectinput.click( wincap.window_rect[0]+528, wincap.window_rect[1]+433) def click_no(gamename): wincap = WindowCapture(gamename) pydirectinput.click( wincap.window_rect[0]+763, wincap.window_rect[1]+433) def click_otherworld_ok(gamename): wincap = WindowCapture(gamename) pydirectinput.click( wincap.window_rect[0]+503, wincap.window_rect[1]+487) def click_otherworld_no(gamename): wincap = WindowCapture(gamename) pydirectinput.click( wincap.window_rect[0]+778, wincap.window_rect[1]+487) def click_choose_map(gamename): wincap = WindowCapture(gamename) pydirectinput.click( wincap.window_rect[0]+1150, wincap.window_rect[1]+210) def click_explore_again(gamename): wincap = WindowCapture(gamename) pydirectinput.click( wincap.window_rect[0]+1150, wincap.window_rect[1]+152) def click_back_to_town(gamename): wincap = WindowCapture(gamename) pydirectinput.click( wincap.window_rect[0]+1150, wincap.window_rect[1]+328) def click_map_number(gamename, mapnum): wincap = WindowCapture(gamename) map_to_clickpoints = { 5: (728, 521), 6: (640, 631), 7: (605, 455), 8: (542, 350), 9: (293, 297), 10: (777, 406), 11: (140, 370), 12: (500, 246), 13: (500, 672), 14: (419, 478), 15: (423, 263), 16: (563, 562), 17: (642, 432), 18: (249, 325) } x, y = map_to_clickpoints[mapnum] pydirectinput.click(wincap.window_rect[0]+x, wincap.window_rect[1]+y) def choose_difficulty_and_enter(gamename, diff): wincap = WindowCapture(gamename) num_clicks = 0 if diff == "N": num_clicks = 0 elif diff == "H": num_clicks = 1 elif diff == "VH": num_clicks == 2 elif diff == "BM": num_clicks == 3 for i in range(num_clicks): pydirectinput.click( wincap.window_rect[0]+618, wincap.window_rect[1]+333) time.sleep(0.3) # Then click on enter dungeon pydirectinput.click( wincap.window_rect[0]+1033, wincap.window_rect[1]+736) def go_to_change_character(gamename): if not BotUtils.detect_menu_open(gamename): pydirectinput.press('esc') wincap = WindowCapture(gamename) pydirectinput.click( wincap.window_rect[0]+640, wincap.window_rect[1]+363) def exit_game(gamename): if not BotUtils.detect_menu_open(gamename): pydirectinput.press('esc') wincap = WindowCapture(gamename) pydirectinput.click( wincap.window_rect[0]+640, wincap.window_rect[1]+480) time.sleep(0.2) pydirectinput.click( wincap.window_rect[0]+640, wincap.window_rect[1]+428) def choose_character(gamename, charnum): wincap = WindowCapture(gamename) char_clickpoints = { 1: (1100, 140), 2: (1100, 210), 3: (1100, 280), 4: (1100, 350), 5: (1100, 420), 6: (1100, 490), 7: (1100, 560), 8: (1100, 630) } if charnum > 8: pydirectinput.click( wincap.window_rect[0]+1165, wincap.window_rect[1]+680) x, y = char_clickpoints[charnum-8] else: pydirectinput.click( wincap.window_rect[0]+1035, wincap.window_rect[1]+680) x, y = char_clickpoints[charnum] time.sleep(0.2) pydirectinput.click(wincap.window_rect[0]+x, wincap.window_rect[1]+y) time.sleep(0.2) pydirectinput.click( wincap.window_rect[0]+640, wincap.window_rect[1]+765) class Vision: def __init__(self, needle_img_path, method=cv2.TM_CCOEFF_NORMED): self.needle_img = cv2.imread(needle_img_path, cv2.IMREAD_UNCHANGED) self.needle_w = self.needle_img.shape[1] self.needle_h = self.needle_img.shape[0] # TM_CCOEFF, TM_CCOEFF_NORMED, TM_CCORR, TM_CCORR_NORMED, TM_SQDIFF, TM_SQDIFF_NORMED self.method = method def find(self, haystack_img, threshold=0.7, max_results=15, epsilon=0.5): result = cv2.matchTemplate(haystack_img, self.needle_img, self.method) locations = np.where(result >= threshold) locations = list(zip(*locations[::-1])) if not locations: return np.array([], dtype=np.int32).reshape(0, 4) rectangles = [] for loc in locations: rect = [int(loc[0]), int(loc[1]), self.needle_w, self.needle_h] rectangles.append(rect) rectangles.append(rect) rectangles, weights = cv2.groupRectangles( rectangles, groupThreshold=1, eps=epsilon) return rectangles def get_click_points(self, rectangles): points = [] for (x, y, w, h) in rectangles: center_x = x + int(w/2) center_y = y + int(h/2) points.append((center_x, center_y)) return points def draw_rectangles(self, haystack_img, rectangles): # BGR line_color = (0, 255, 0) line_type = cv2.LINE_4 for (x, y, w, h) in rectangles: top_left = (x, y) bottom_right = (x + w, y + h) cv2.rectangle(haystack_img, top_left, bottom_right, line_color, lineType=line_type) return haystack_img def draw_crosshairs(self, haystack_img, points): # BGR marker_color = (255, 0, 255) marker_type = cv2.MARKER_CROSS for (center_x, center_y) in points: cv2.drawMarker(haystack_img, (center_x, center_y), marker_color, marker_type) return haystack_img class DynamicFilter: TRACKBAR_WINDOW = "Trackbars" # create gui window with controls for adjusting arguments in real-time def __init__(self, needle_img_path, method=cv2.TM_CCOEFF_NORMED): self.needle_img = cv2.imread(needle_img_path, cv2.IMREAD_UNCHANGED) self.needle_w = self.needle_img.shape[1] self.needle_h = self.needle_img.shape[0] # TM_CCOEFF, TM_CCOEFF_NORMED, TM_CCORR, TM_CCORR_NORMED, TM_SQDIFF, TM_SQDIFF_NORMED self.method = method def find(self, haystack_img, threshold=0.7, epsilon=0.5): result = cv2.matchTemplate(haystack_img, self.needle_img, self.method) locations = np.where(result >= threshold) locations = list(zip(*locations[::-1])) if not locations: return np.array([], dtype=np.int32).reshape(0, 4) rectangles = [] for loc in locations: rect = [int(loc[0]), int(loc[1]), self.needle_w, self.needle_h] rectangles.append(rect) rectangles.append(rect) rectangles, weights = cv2.groupRectangles( rectangles, groupThreshold=1, eps=epsilon) return rectangles def get_click_points(self, rectangles): points = [] for (x, y, w, h) in rectangles: center_x = x + int(w/2) center_y = y + int(h/2) points.append((center_x, center_y)) return points def draw_rectangles(self, haystack_img, rectangles): # BGR line_color = (0, 255, 0) line_type = cv2.LINE_4 for (x, y, w, h) in rectangles: top_left = (x, y) bottom_right = (x + w, y + h) cv2.rectangle(haystack_img, top_left, bottom_right, line_color, lineType=line_type) return haystack_img def draw_crosshairs(self, haystack_img, points): # BGR marker_color = (255, 0, 255) marker_type = cv2.MARKER_CROSS for (center_x, center_y) in points: cv2.drawMarker(haystack_img, (center_x, center_y), marker_color, marker_type) return haystack_img def init_control_gui(self): cv2.namedWindow(self.TRACKBAR_WINDOW, cv2.WINDOW_NORMAL) cv2.resizeWindow(self.TRACKBAR_WINDOW, 350, 700) # required callback. we'll be using getTrackbarPos() to do lookups # instead of using the callback. def nothing(position): pass # create trackbars for bracketing. # OpenCV scale for HSV is H: 0-179, S: 0-255, V: 0-255 cv2.createTrackbar('HMin', self.TRACKBAR_WINDOW, 0, 179, nothing) cv2.createTrackbar('SMin', self.TRACKBAR_WINDOW, 0, 255, nothing) cv2.createTrackbar('VMin', self.TRACKBAR_WINDOW, 0, 255, nothing) cv2.createTrackbar('HMax', self.TRACKBAR_WINDOW, 0, 179, nothing) cv2.createTrackbar('SMax', self.TRACKBAR_WINDOW, 0, 255, nothing) cv2.createTrackbar('VMax', self.TRACKBAR_WINDOW, 0, 255, nothing) # Set default value for Max HSV trackbars cv2.setTrackbarPos('HMax', self.TRACKBAR_WINDOW, 179) cv2.setTrackbarPos('SMax', self.TRACKBAR_WINDOW, 255) cv2.setTrackbarPos('VMax', self.TRACKBAR_WINDOW, 255) # trackbars for increasing/decreasing saturation and value cv2.createTrackbar('SAdd', self.TRACKBAR_WINDOW, 0, 255, nothing) cv2.createTrackbar('SSub', self.TRACKBAR_WINDOW, 0, 255, nothing) cv2.createTrackbar('VAdd', self.TRACKBAR_WINDOW, 0, 255, nothing) cv2.createTrackbar('VSub', self.TRACKBAR_WINDOW, 0, 255, nothing) # returns an HSV filter object based on the control GUI values def get_hsv_filter_from_controls(self): # Get current positions of all trackbars hsv_filter = HsvFilter() hsv_filter.hMin = cv2.getTrackbarPos('HMin', self.TRACKBAR_WINDOW) hsv_filter.sMin = cv2.getTrackbarPos('SMin', self.TRACKBAR_WINDOW) hsv_filter.vMin = cv2.getTrackbarPos('VMin', self.TRACKBAR_WINDOW) hsv_filter.hMax = cv2.getTrackbarPos('HMax', self.TRACKBAR_WINDOW) hsv_filter.sMax = cv2.getTrackbarPos('SMax', self.TRACKBAR_WINDOW) hsv_filter.vMax = cv2.getTrackbarPos('VMax', self.TRACKBAR_WINDOW) hsv_filter.sAdd = cv2.getTrackbarPos('SAdd', self.TRACKBAR_WINDOW) hsv_filter.sSub = cv2.getTrackbarPos('SSub', self.TRACKBAR_WINDOW) hsv_filter.vAdd = cv2.getTrackbarPos('VAdd', self.TRACKBAR_WINDOW) hsv_filter.vSub = cv2.getTrackbarPos('VSub', self.TRACKBAR_WINDOW) return hsv_filter def apply_hsv_filter(self, original_image, hsv_filter=None): hsv = cv2.cvtColor(original_image, cv2.COLOR_BGR2HSV) if not hsv_filter: hsv_filter = self.get_hsv_filter_from_controls() h, s, v = cv2.split(hsv) s = BotUtils.shift_channel(s, hsv_filter.sAdd) s = BotUtils.shift_channel(s, -hsv_filter.sSub) v = BotUtils.shift_channel(v, hsv_filter.vAdd) v = BotUtils.shift_channel(v, -hsv_filter.vSub) hsv = cv2.merge([h, s, v]) lower = np.array([hsv_filter.hMin, hsv_filter.sMin, hsv_filter.vMin]) upper = np.array([hsv_filter.hMax, hsv_filter.sMax, hsv_filter.vMax]) mask = cv2.inRange(hsv, lower, upper) result = cv2.bitwise_and(hsv, hsv, mask=mask) img = cv2.cvtColor(result, cv2.COLOR_HSV2BGR) return img class SellRepair(): def __init__(self, rarity_cutoff=1, last_row_protect=True) -> None: # rarities are as follows: # nocolour=0, green=1, blue=2 self.cutoff = rarity_cutoff # this is for whether lastrow in equip is protected # useful for characters levelling with next upgrades ready self.last_row_protect = last_row_protect with open("gamename.txt") as f: self.gamename = f.readline() self.inventory_wincap = WindowCapture( self.gamename, [512, 277, 775, 430]) # This is for correct mouse positioning self.game_wincap = WindowCapture(self.gamename) self.shop_check_wincap = WindowCapture( self.gamename, [274, 207, 444, 208]) # These are for holding reference rgb values # Using sets as can then compare easily to other sets self.empty = {41, 45, 50} self.rar_green = {2, 204, 43} self.rar_blue = {232, 144, 5} self.rar_none = {24, 33, 48} self.junk_list = self.grab_junk_list() def grab_junk_list(self): jl = [] with open("itemrgb.txt") as f: lines = f.readlines() for line in lines: _, rgb = line.split("|") r, g, b = rgb.split(",") jl.append({int(r), int(g), int(b)}) return jl def ident_sell_repair(self): self.game_wincap.update_window_position(border=False) self.shop_check_wincap.update_window_position(border=False) self.open_store_if_necessary() # First go through all the equipment self.change_tab("Equipment") # time.sleep(0.2) # self.hover_mouse_all() time.sleep(0.3) screenshot = self.inventory_wincap.get_screenshot() non_empty = self.remove_empty(screenshot) junk_list = self.identify_rarities_equip(non_empty, screenshot) self.sell(junk_list, "Equipment") # Then go through all the other loot self.change_tab("Other") # time.sleep(0.2) # self.hover_mouse_all() time.sleep(0.3) screenshot = self.inventory_wincap.get_screenshot() non_empty = self.remove_empty(screenshot) junk_list = self.identify_items_other(non_empty, screenshot) self.sell(junk_list) # and finally repair gear self.repair() # and now go through all the steps again minus repair to make sure self.change_tab("Equipment") time.sleep(0.3) screenshot = self.inventory_wincap.get_screenshot() non_empty = self.remove_empty(screenshot) junk_list = self.identify_rarities_equip(non_empty, screenshot) self.sell(junk_list, "Equipment") self.change_tab("Other") time.sleep(0.3) screenshot = self.inventory_wincap.get_screenshot() non_empty = self.remove_empty(screenshot) junk_list = self.identify_items_other(non_empty, screenshot) self.sell(junk_list) def open_store_if_necessary(self): # This will search to see if the inventory is open # in the correct spot and then click shop if not screenshot = self.shop_check_wincap.get_screenshot() pix1 = screenshot[0, 0] pix1 = int(pix1[0]) + int(pix1[1]) + int(pix1[2]) pix2 = screenshot[0, 169] pix2 = int(pix2[0]) + int(pix2[1]) + int(pix2[2]) if pix1 == 103 and pix2 == 223: pass else: # need to open the store self.game_wincap.update_window_position(border=False) offsetx = self.game_wincap.window_rect[0] + 534 offsety = self.game_wincap.window_rect[1] + 277 ctypes.windll.user32.SetCursorPos(offsetx+610, offsety-10) ctypes.windll.user32.mouse_event( 0x0002, 0, 0, 0, 0) ctypes.windll.user32.mouse_event( 0x0004, 0, 0, 0, 0) def change_tab(self, name): self.game_wincap.update_window_position(border=False) x = self.game_wincap.window_rect[0] + 534-60 if name == "Equipment": y = self.game_wincap.window_rect[1] + 277 - 15 elif name == "Other": y = self.game_wincap.window_rect[1] + 277 + 44 ctypes.windll.user32.SetCursorPos(x, y) ctypes.windll.user32.mouse_event( 0x0002, 0, 0, 0, 0) ctypes.windll.user32.mouse_event( 0x0004, 0, 0, 0, 0) def hover_mouse_all(self): self.game_wincap.update_window_position(border=False) offsetx = self.game_wincap.window_rect[0] + 534 offsety = self.game_wincap.window_rect[1] + 277 for i in range(4): for j in range(6): x = offsetx+j*44 y = offsety+i*44 ctypes.windll.user32.SetCursorPos(x-10, y) time.sleep(0.03) ctypes.windll.user32.SetCursorPos(x, y) time.sleep(0.03) ctypes.windll.user32.SetCursorPos(x+10, y) ctypes.windll.user32.SetCursorPos(offsetx, offsety-70) # ctypes.windll.user32.SetCursorPos(offsetx+610, offsety-10) def remove_empty(self, screenshot): non_empty = [] for i in range(4): for j in range(6): colour = set(screenshot[i*44, 22+j*44]) if colour != self.empty: non_empty.append([i, j]) # format will be as follows of return list # x,y,r,g,b return non_empty def identify_rarities_equip(self, rowcol_list, screenshot): junk = [] for rowcol in rowcol_list: colour = set(screenshot[rowcol[0]*44, rowcol[1]*44]) if colour == self.rar_none: junk.append([rowcol[0], rowcol[1]]) elif colour == self.rar_green: if self.cutoff >= 1: junk.append([rowcol[0], rowcol[1]]) elif colour == self.rar_green: if self.cutoff >= 2: junk.append([rowcol[0], rowcol[1]]) # format will be as follows of return list # x,y corresponding to row,col return junk def identify_items_other(self, rowcol_list, screenshot): junk = [] for rowcol in rowcol_list: colour = set(screenshot[rowcol[0]*44, 22+rowcol[1]*44]) if colour in self.junk_list: junk.append([rowcol[0], rowcol[1]]) # format will be as follows of return list # x,y corresponding to row,col return junk def sell(self, rowcol_list, tab="Other"): offsetx = self.game_wincap.window_rect[0] + 534 offsety = self.game_wincap.window_rect[1] + 277 for item in rowcol_list: if tab == "Equipment": if self.last_row_protect: if item[0] == 3: continue x = offsetx+item[1]*44 y = offsety+item[0]*44 ctypes.windll.user32.SetCursorPos(x, y) time.sleep(0.1) ctypes.windll.user32.mouse_event( 0x0008, 0, 0, 0, 0) time.sleep(0.01) ctypes.windll.user32.mouse_event( 0x0010, 0, 0, 0, 0) # Then click a second time to be sure time.sleep(0.01) ctypes.windll.user32.mouse_event( 0x0008, 0, 0, 0, 0) time.sleep(0.01) ctypes.windll.user32.mouse_event( 0x0010, 0, 0, 0, 0) def repair(self): self.game_wincap.update_window_position(border=False) offsetx = self.game_wincap.window_rect[0] + 534 offsety = self.game_wincap.window_rect[1] + 277 ctypes.windll.user32.SetCursorPos(offsetx-310, offsety+325) ctypes.windll.user32.mouse_event( 0x0002, 0, 0, 0, 0) ctypes.windll.user32.mouse_event( 0x0004, 0, 0, 0, 0) ctypes.windll.user32.SetCursorPos(offsetx+0, offsety+180) ctypes.windll.user32.mouse_event( 0x0002, 0, 0, 0, 0) ctypes.windll.user32.mouse_event( 0x0004, 0, 0, 0, 0) # this is if everything is already repaired ctypes.windll.user32.SetCursorPos(offsetx+100, offsety+180) ctypes.windll.user32.mouse_event( 0x0002, 0, 0, 0, 0) ctypes.windll.user32.mouse_event( 0x0004, 0, 0, 0, 0) class QuestHandle(): def __init__(self) -> None: with open("gamename.txt") as f: gamename = f.readline() self.game_wincap = WindowCapture(gamename) self.white_text_filter = HsvFilter( 0, 0, 102, 45, 65, 255, 0, 0, 0, 0) self.yellow_text_filter = HsvFilter( 16, 71, 234, 33, 202, 255, 0, 0, 0, 0) self.blue_text_filter = HsvFilter( 83, 126, 85, 102, 255, 255, 0, 0, 0, 0) self.all_text_filter = HsvFilter( 0, 0, 61, 78, 255, 255, 0, 255, 0, 0) self.vision = Vision('xprompt67filtv2.jpg') self.accept_rect = [725, 525, 925, 595] self.accept_wincap = WindowCapture(gamename, self.accept_rect) self.skip_rect = [730, 740, 890, 780] self.skip_wincap = WindowCapture(gamename, self.skip_rect) self.next_rect = [880, 740, 1040, 780] self.next_wincap = WindowCapture(gamename, self.next_rect) self.quest_rect = [310, 160, 1055, 650] self.quest_wincap = WindowCapture(gamename, self.quest_rect) self.questlist_rect = [740, 240, 1050, 580] self.questlist_wincap = WindowCapture(gamename, self.questlist_rect) self.complete_wincap = WindowCapture(gamename, self.next_rect) self.xprompt_rect = [1130, 670, 1250, 720] self.xprompt_wincap = WindowCapture(gamename, self.xprompt_rect) def start_quest_handle(self): start_time = time.time() while time.time() < start_time + 2: if self.check_for_accept(): break def convert_and_click(self, x, y, rect): self.game_wincap.update_window_position(border=False) truex = int(x + self.game_wincap.window_rect[0] + rect[0]) truey = int(y + self.game_wincap.window_rect[1] + rect[1]) ctypes.windll.user32.SetCursorPos(truex, truey) ctypes.windll.user32.mouse_event( 0x0002, 0, 0, 0, 0) ctypes.windll.user32.mouse_event( 0x0004, 0, 0, 0, 0) def check_for_accept(self): image = self.accept_wincap.get_screenshot() image = self.vision.apply_hsv_filter( image, self.white_text_filter) rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) results = pytesseract.image_to_data( rgb, output_type=pytesseract.Output.DICT, lang='eng') detection = False for i in range(0, len(results["text"])): if "Accept" in results["text"][i]: x = results["left"][i] + (results["width"][i]/2) y = results["top"][i] + (results["height"][i]/2) self.convert_and_click(x, y, self.accept_rect) detection = True break if not detection: return self.check_for_skip() else: return True def check_for_skip(self): image = self.skip_wincap.get_screenshot() image = self.vision.apply_hsv_filter( image, self.white_text_filter) rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) results = pytesseract.image_to_data( rgb, output_type=pytesseract.Output.DICT, lang='eng') detection = False for i in range(0, len(results["text"])): if "Skip" in results["text"][i]: x = results["left"][i] + (results["width"][i]/2) y = results["top"][i] + (results["height"][i]/2) self.convert_and_click(x, y, self.skip_rect) detection = True break if not detection: return self.check_for_next() else: return True def check_for_next(self): image = self.next_wincap.get_screenshot() image = self.vision.apply_hsv_filter( image, self.white_text_filter) rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) results = pytesseract.image_to_data( rgb, output_type=pytesseract.Output.DICT, lang='eng') detection = False for i in range(0, len(results["text"])): if "Next" in results["text"][i]: x = results["left"][i] + (results["width"][i]/2) y = results["top"][i] + (results["height"][i]/2) self.convert_and_click(x, y, self.next_rect) detection = True break if not detection: return self.check_for_quest() else: return True def check_for_quest(self): image = self.quest_wincap.get_screenshot() image = self.vision.apply_hsv_filter( image, self.white_text_filter) rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) tess_config = '--psm 6 --oem 3 -c tessedit_char_whitelist=Quest' results = pytesseract.image_to_data( rgb, output_type=pytesseract.Output.DICT, lang='eng', config=tess_config) detection = False for i in range(0, len(results["text"])): if "Quest" in results["text"][i]: x = results["left"][i] + (results["width"][i]/2) y = results["top"][i] + (results["height"][i]/2) self.convert_and_click(x, y, self.quest_rect) detection = True break if not detection: return self.check_for_questlist() else: return True def check_for_questlist(self): image = self.questlist_wincap.get_screenshot() image = self.vision.apply_hsv_filter( image, self.all_text_filter) rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) results = pytesseract.image_to_data( rgb, output_type=pytesseract.Output.DICT, lang='eng') detection = False for i in range(0, len(results["text"])): if "LV" in results["text"][i]: # at this point need to grab the centre of the rect x = results["left"][i] + (results["width"][i]/2) y = results["top"][i] + (results["height"][i]/2) # and then click at this position self.convert_and_click(x, y, self.questlist_rect) detection = True break if not detection: return self.check_for_complete() else: return True def check_for_complete(self): image = self.complete_wincap.get_screenshot() image = self.vision.apply_hsv_filter( image, self.white_text_filter) rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) results = pytesseract.image_to_data( rgb, output_type=pytesseract.Output.DICT, lang='eng') detection = False for i in range(0, len(results["text"])): if "Com" in results["text"][i]: x = results["left"][i] + (results["width"][i]/2) y = results["top"][i] + (results["height"][i]/2) self.convert_and_click(x, y, self.next_rect) detection = True break if not detection: return self.check_for_xprompt() else: return True def check_for_xprompt(self): image = self.xprompt_wincap.get_screenshot() image = self.vision.apply_hsv_filter( image, self.blue_text_filter) rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) results = pytesseract.image_to_data( rgb, output_type=pytesseract.Output.DICT, lang='eng') detection = False for i in range(0, len(results["text"])): if "Press" in results["text"][i]: pydirectinput.keyDown("x") time.sleep(0.1) pydirectinput.keyUp("x") detection = True break if not detection: return False else: return True class Follower(): def __init__(self) -> None: self.pressed_keys = [] self.relx = 0 self.rely = 0 def navigate_towards(self, x, y): self.relx = x self.rely = y if self.relx > 1: # Check if opposite key held down if "left" in self.pressed_keys: self.pressed_keys.remove("left") CustomInput.release_key(CustomInput.key_map["left"], "left") # Check that not already being held down if "right" not in self.pressed_keys: self.pressed_keys.append("right") # Hold the key down CustomInput.press_key(CustomInput.key_map["right"], "right") elif self.relx < -1: # Check if opposite key held down if "right" in self.pressed_keys: self.pressed_keys.remove("right") CustomInput.release_key(CustomInput.key_map["right"], "right") # Check that not already being held down if "left" not in self.pressed_keys: self.pressed_keys.append("left") # Hold the key down CustomInput.press_key(CustomInput.key_map["left"], "left") else: # Handling for case where = 0, need to remove both keys if "right" in self.pressed_keys: self.pressed_keys.remove("right") CustomInput.release_key(CustomInput.key_map["right"], "right") if "left" in self.pressed_keys: self.pressed_keys.remove("left") CustomInput.release_key(CustomInput.key_map["left"], "left") # Handling for y-dir next if self.rely > 1: # Check if opposite key held down if "down" in self.pressed_keys: self.pressed_keys.remove("down") CustomInput.release_key(CustomInput.key_map["down"], "down") # Check that not already being held down if "up" not in self.pressed_keys: self.pressed_keys.append("up") # Hold the key down CustomInput.press_key(CustomInput.key_map["up"], "up") elif self.rely < -1: # Check if opposite key held down if "up" in self.pressed_keys: self.pressed_keys.remove("up") CustomInput.release_key(CustomInput.key_map["up"], "up") # Check that not already being held down if "down" not in self.pressed_keys: self.pressed_keys.append("down") # Hold the key down CustomInput.press_key(CustomInput.key_map["down"], "down") else: # Handling for case where = 0, need to remove both keys if "up" in self.pressed_keys: self.pressed_keys.remove("up") CustomInput.release_key(CustomInput.key_map["up"], "up") if "down" in self.pressed_keys: self.pressed_keys.remove("down") CustomInput.release_key(CustomInput.key_map["down"], "down") if __name__ == "__main__": time.sleep(2) with open("gamename.txt") as f: gamename = f.readline() # start = time.time() # BotUtils.detect_xprompt(gamename) # print("Time taken: {}s".format(time.time()-start)) BotUtils.close_map_and_menu(gamename)

39.910107

130

0.562536

10,401

85,687

4.47707

0.10124

0.005369

0.023708

0.01675

0.640081

0.603294

0.56668

0.540137

0.505949

0.483894

0

0.045521

0.332151

85,687

2,146

131

39.928705

0.7682

0.067396

0

0.561884

0

0.030656

0.007185

0

0.001204

0

1

0.066265

false

0.002191

0.009858

0.001095

0.152793

0.003834

0

null

0

null

0

1

0

fab81a4ff4643ede4d0508d54ffa75a9959d0825

2,072

py

Python

03-basic-collections-and-for/07s-solution-parsing-reddit.py

Tebs-Lab/python-for-scripting-workshop

8e3aeb99f95112143701926aa7ab495358c4e3ee

[ "Unlicense" ]

null

03-basic-collections-and-for/07s-solution-parsing-reddit.py

Tebs-Lab/python-for-scripting-workshop

8e3aeb99f95112143701926aa7ab495358c4e3ee

[ "Unlicense" ]

null

03-basic-collections-and-for/07s-solution-parsing-reddit.py

Tebs-Lab/python-for-scripting-workshop

8e3aeb99f95112143701926aa7ab495358c4e3ee

[ "Unlicense" ]

null

import json import pathlib import ssl from urllib.request import Request, urlopen # Change this to False to use the file data. use_live_data = True if use_live_data: # Fetching the live data from reddit. url = "http://www.reddit.com/r/aww.json" request = Request( url, headers={ 'User-Agent': 'TebsLabPythonExercise/0.0.1' # setting the user agent decreases throttling by Reddit } ) # Context is for MacOS users related to SSL certificates. Details: https://clay-atlas.com/us/blog/2021/09/26/python-en-urllib-error-ssl-certificate/ response = urlopen(request, context=ssl._create_unverified_context()) listing = json.load(response) else: # Alternatively, loading the data from the provided json file. containing_dir = pathlib.Path(__file__).parent.resolve() with open(pathlib.Path(containing_dir / 'supplemental-materials' / 'reddit-aww.json')) as json_file: listing = json.load(json_file) # Extract the posts to loop over them posts = listing['data']['children'] # For our fact finding mission posts_by_user = {} sum_of_upvote_ratio = 0 # Iterate over the posts, extract the data, print for post in posts: post_data = post['data'] title = post_data['title'] username = post_data['author'] upvote_ratio = post_data['upvote_ratio'] post_url = post_data['url'] # Check if the user is already in there if username not in posts_by_user: posts_by_user[username] = 0 # Then increase their post count. posts_by_user[username] += 1 sum_of_upvote_ratio += upvote_ratio print('================') print(f'Title: {title}\nUser: {username}\nUpvote Ratio: {upvote_ratio}\nURL: {post_url}') print() # Display which users posted multiple times (if any) for username, post_count in posts_by_user.items(): if post_count > 1: print(f'{username} posted {post_count} times!') # Compute the avg upvote ratio avg_upvote_ratio = sum_of_upvote_ratio / len(posts) print(f'The average upvote ratio was {avg_upvote_ratio}')

32.375

152

0.696911

296

2,072

4.702703

0.408784

0.086925

0.039511

0.034483

0

0.008977

0.193533

2,072

63

153

32.888889

0.824057

0.291023

0

0.025

0.224588

0.033654

0

1

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false

0

0.1

0

0.1

0.125

0

null

0

null

0

1

0

faba9d7e91fbeec825bc33f3a7bfbbe7e75ec534

501

py

Python

src/topology/all_gates/qft.py

Dreamonic/shor-algorithm

19a4d95f0f19809cd3fe1db4d834ff3a02fba68d

[ "MIT" ]

null

src/topology/all_gates/qft.py

Dreamonic/shor-algorithm

19a4d95f0f19809cd3fe1db4d834ff3a02fba68d

[ "MIT" ]

null

src/topology/all_gates/qft.py

Dreamonic/shor-algorithm

19a4d95f0f19809cd3fe1db4d834ff3a02fba68d

[ "MIT" ]

null

from projectq.meta import Dagger from projectq.ops import H, CRz from src.shared.rotate import calculate_phase def qft(eng, circuit, qubits): m = len(qubits) for i in range(m - 1, -1, -1): circuit.apply_single_qubit_gate(H, qubits[i]) for j in range(2, i + 2): circuit.apply_ld_two_qubit_gate(CRz(calculate_phase(j)), qubits[i - j + 1], qubits[i]) def qft_inverse(eng, circuit, qubits): with Dagger(eng): qft(eng, circuit, qubits)

27.833333

99

0.636727

78

501

3.961538

0.448718

0.097087

0.15534

0.122977

0

0.015873

0.245509

501

17

100

29.470588

0.801587

0

1

0.166667

false

0

0.25

0

0.416667

0

null

0

null

0

1

0

fabb5ec294332474767da7f125ccef5a1b6a0eb2

1,777

py

Python

src/chapter_3/report.py

gm672/memoire

8d8c288e7996119aba89ff7c61a78641840a1206

[ "RSA-MD" ]

null

src/chapter_3/report.py

gm672/memoire

8d8c288e7996119aba89ff7c61a78641840a1206

[ "RSA-MD" ]

null

src/chapter_3/report.py

gm672/memoire

8d8c288e7996119aba89ff7c61a78641840a1206

[ "RSA-MD" ]

null

import dl import dlm import conll3 import csv import argparse import textwrap def create(fileName1): # store the dependency tree in a dict T = conll3.conllFile2trees(fileName1) stats = [] c=0 for tree in T: c+=1 l = len(tree) # length d = dl.DL_T(tree) # observed sentence #true random r = dl.true_random(tree) dr = dl.DL_L(r,tree) #optimal linearization = dlm.optimal_linearization(tree) dmin = dl.DL_L(linearization,tree) omega = dl.omega(dmin,dr,d,l) # Omega gamma = dl.gamma(dmin,d) # Gamma mdd = dl.MDD(d,l) # MDD stats.append([l,d,dr,dmin,omega,gamma,mdd]) # Create the csv line return stats parser = argparse.ArgumentParser(description='Get a csv file with DL measure from a CONLL file. --help for more information',formatter_class=argparse.RawTextHelpFormatter) parser.add_argument('file', help= textwrap.dedent('''\ The file to be analysed ''')) parser.add_argument('output', help= textwrap.dedent('''\ Output file. The CSV file does not have headers. It is formated as : length, actual dependency length, random DL , minimum DL, omega, gamma, MDD ''')) args = parser.parse_args() print(args.file) print(args.output) stats = create(args.file) with open(args.output, 'w+',newline='') as csvfile: spamwriter = csv.writer(csvfile, delimiter=',') for line in stats: spamwriter.writerow(line) csvfile.close()

26.522388

172

0.549803

208

1,777

4.653846

0.418269

0.012397

0.010331

0

0.006082

0.352279

1,777

66

173

26.924242

0.834926

0.064716

0

0.090909

0

0.272785

0

1

0.022727

false

0

0.136364

0

0.181818

0.045455

0

null

0

null

0

1

0

fabb6360664af2d6876a95c3edbd58b72fce2dcc

3,323

py

Python

scripts/inventory.py

Otus-DevOps-2019-08/ntikhomirov_infra

428121d5b4ff13c508441d147d738fce72ca5590

[ "MIT" ]

1

2019-09-27T10:14:04.000Z

scripts/inventory.py

Otus-DevOps-2019-08/ntikhomirov_infra

428121d5b4ff13c508441d147d738fce72ca5590

[ "MIT" ]

9

2019-10-02T09:29:18.000Z

2019-11-22T12:43:41.000Z

scripts/inventory.py

Otus-DevOps-2019-08/ntikhomirov_infra

428121d5b4ff13c508441d147d738fce72ca5590

[ "MIT" ]

null

#!/usr/bin/env python3.6 import os import sys import argparse try: import json except ImportError: import simplejson as json gce = True evn = 'prod' count = 0 #Подключаем модули для использования API GCE try: from googleapiclient import discovery from oauth2client.client import GoogleCredentials except Exception as e: import yaml gce = False class Inventory(object): gce = "" def __init__(self): self.inventory = {} self.read_cli_args() if self.args.list: self.inventory = self.dynamic_inventory() elif self.args.host: self.inventory = self.empty_inventory() else: self.inventory = self.empty_inventory() print(json.dumps(self.inventory)); def empty_inventory(self): return {'_meta': {'hostvars': {}}} def dynamic_inventory(self): counta = 0 inventory = { 'app': { 'hosts': [], 'vars': {} }, 'db': { 'hosts': [], 'vars': {} }, 'proxy': { 'hosts': [], 'vars': {} }, '_meta': { } } if gce: credentials = GoogleCredentials.get_application_default() service = discovery.build('compute', 'v1', credentials=credentials) # Возможно надо убрать в конфиг project = 'indigo-almanac-254221' # Возможно надо убрать в конфиг zone = 'europe-west4-a' request = service.instances().list(project=project, zone=zone) while request is not None: response = request.execute() #Производим выборку по tags из инстансов которые созданы в gcloud for instance in response['items']: if 'items' in instance['tags']: t = instance['tags']['items'] for i in t: if str(i)== 'db' : inventory['db']['hosts'].append(instance['name']) for j in instance['networkInterfaces'] : inventory['app']['vars']['db_url'] = str(j['networkIP']) elif str(i) == 'app': inventory['app']['hosts'].append(instance['name']) counta += 1 elif str(i) == 'proxy': inventory['proxy']['hosts'].append(instance['name']) elif str(i) == 'prod' or str(i) == 'test': inventory['app']['vars']['env'] = str(i) inventory['proxy']['vars']['env'] = str(i) inventory['db']['vars']['env'] = str(i) request = service.instances().list_next(previous_request=request, previous_response=response) inventory['proxy']['vars']['count'] = str(counta) return inventory else: with open('./inventory.yml') as f: print(json.dumps(yaml.load(f))) def read_cli_args(self): parser = argparse.ArgumentParser() parser.add_argument('--list', action = 'store_true') parser.add_argument('--host', action = 'store') self.args = parser.parse_args() Inventory()

29.936937

106

0.504664

322

3,323

5.130435

0.406832

0.01937

0.041162

0.041768

0.09201

0

0.006629

0.36443

3,323

110

107

30.209091

0.775568

0.057177

0

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0

0.098146

0.006714

0

1

0.047059

false

0

0.105882

0.011765

0.2

0.023529

0

null

0

null

0

1

0

fabbe3032884a2ee4ff62a5a09e013558b6d077f

3,689

py

Python

tests/test.py

ecmwf/pyfdb

90716ddcaa8b3d981e695b47a1690123e0c230ba

[ "Apache-2.0" ]

null

tests/test.py

ecmwf/pyfdb

90716ddcaa8b3d981e695b47a1690123e0c230ba

[ "Apache-2.0" ]

null

tests/test.py

ecmwf/pyfdb

90716ddcaa8b3d981e695b47a1690123e0c230ba

[ "Apache-2.0" ]

null

#!/usr/bin/env python # (C) Copyright 1996- ECMWF. # This software is licensed under the terms of the Apache Licence Version 2.0 # which can be obtained at http://www.apache.org/licenses/LICENSE-2.0. # In applying this licence, ECMWF does not waive the privileges and immunities # granted to it by virtue of its status as an intergovernmental organisation nor # does it submit to any jurisdiction. import shutil from pyeccodes import Reader import pyfdb fdb = pyfdb.FDB() # Archive # key = { "domain": "g", "stream": "oper", "levtype": "pl", "levelist": "300", "date": "20191110", "time": "0000", "step": "0", "param": "138", "class": "rd", "type": "an", "expver": "xxxx", } filename = "x138-300.grib" fdb.archive(open(filename, "rb").read(), key) key["levelist"] = "400" filename = "x138-400.grib" pyfdb.archive(open(filename, "rb").read()) key["expver"] = "xxxy" filename = "y138-400.grib" fdb.archive(open(filename, "rb").read()) fdb.flush() # List # request = { "class": "rd", "expver": "xxxx", "stream": "oper", "date": "20191110", "time": "0000", "domain": "g", "type": "an", "levtype": "pl", "step": 0, "levelist": [300, "500"], "param": ["138", 155, "t"], } print("direct function, request as dictionary:", request) for el in pyfdb.list(request): print(el) request["levelist"] = ["100", "200", "300", "400", "500", "700", "850", "1000"] request["param"] = "138" print("") print("direct function, updated dictionary:", request) for el in pyfdb.list(request): print(el) # as an alternative, create a FDB instance and start queries from there request["levelist"] = ["400", "500", "700", "850", "1000"] print("") print("fdb object, request as dictionary:", request) for el in fdb.list(request): print(el) # # print('') # print('list ALL:') # for el in fdb.list(): # print(el) # Retrieve # request = { "domain": "g", "stream": "oper", "levtype": "pl", "step": "0", "expver": "xxxx", "date": "20191110", "class": "rd", "levelist": "300", "param": "138", "time": "0000", "type": "an", } filename = "x138-300bis.grib" print("") print("save to file ", filename) with open(filename, "wb") as o, fdb.retrieve(request) as i: shutil.copyfileobj(i, o) request["levelist"] = "400" filename = "x138-400bis.grib" print("save to file ", filename) with open(filename, "wb") as o, fdb.retrieve(request) as i: shutil.copyfileobj(i, o) request["expver"] = "xxxy" filename = "y138-400bis.grib" print("save to file ", filename) with open(filename, "wb") as o, pyfdb.retrieve(request) as i: shutil.copyfileobj(i, o) # request = { # 'class': 'od', # 'expver': '0001', # 'stream': 'oper', # 'date': '20040118', # 'time': '0000', # 'domain': 'g', # 'type': 'an', # 'levtype': 'sfc', # 'step': 0, # 'param': 151 # } print("") print("FDB retrieve") print("direct function, retrieve from request:", request) datareader = pyfdb.retrieve(request) print("") print("reading a small chunk") chunk = datareader.read(10) print(chunk) print("tell()", datareader.tell()) print("go back (partially) - seek(2)") datareader.seek(2) print("tell()", datareader.tell()) print("reading a larger chunk") chunk = datareader.read(40) print(chunk) print("go back - seek(0)") datareader.seek(0) print("") print("decode GRIB") reader = Reader(datareader) grib = next(reader) grib.dump() request["levelist"] = [300, "400"] request["expver"] = "xxxx" filename = "foo.grib" print("") print("save to file ", filename) with open(filename, "wb") as o, fdb.retrieve(request) as i: shutil.copyfileobj(i, o)

21.7

80

0.612632

486

3,689

4.650206

0.308642

0.035398

0.012389

0.026549

0.371681

0.323451

0.286726

0.215044

0.195575

0

0.063434

0.18379

3,689

169

81

21.828402

0.687147

0.204934

0

0.536364

0

0.289456

0

1

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false

0

0.027273

0

0.027273

0.254545

0

null

0

null

0

1

0

fabd3410ff9a1457ff4d27dc4da7e340b30ed724

3,907

py

Python

announcer/pref.py

dokipen/trac-announcer-plugin

7ef4123a7508c5395c8008fa2a8478b1888b4f63

[ "BSD-3-Clause" ]

null

announcer/pref.py

dokipen/trac-announcer-plugin

7ef4123a7508c5395c8008fa2a8478b1888b4f63

[ "BSD-3-Clause" ]

1

2018-06-11T14:48:06.000Z

announcer/pref.py

dokipen/trac-announcer-plugin

7ef4123a7508c5395c8008fa2a8478b1888b4f63

[ "BSD-3-Clause" ]

null

# -*- coding: utf-8 -*- # # Copyright (c) 2008, Stephen Hansen # Copyright (c) 2009, Robert Corsaro # Copyright (c) 2010, Robert Corsaro # # All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are met: # # * Redistributions of source code must retain the above copyright # notice, this list of conditions and the following disclaimer. # * Redistributions in binary form must reproduce the above copyright # notice, this list of conditions and the following disclaimer in the # documentation and/or other materials provided with the distribution. # * Neither the name of the <ORGANIZATION> nor the names of its # contributors may be used to endorse or promote products derived from # this software without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS # "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT # LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR # A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR # CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, # EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, # PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR # PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF # LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING # NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS # SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. # ---------------------------------------------------------------------------- from trac.core import Component, implements, ExtensionPoint from trac.prefs.api import IPreferencePanelProvider from trac.web.chrome import ITemplateProvider, add_stylesheet, Chrome from trac.web import IRequestHandler from pkg_resources import resource_filename from announcer.api import IAnnouncementPreferenceProvider, \ _, tag_, N_ def truth(v): if v in (False, 'False', 'false', 0, '0', ''): return None return True class AnnouncerPreferences(Component): implements(IPreferencePanelProvider, ITemplateProvider) preference_boxes = ExtensionPoint(IAnnouncementPreferenceProvider) def get_htdocs_dirs(self): return [('announcer', resource_filename(__name__, 'htdocs'))] def get_templates_dirs(self): resource_dir = resource_filename(__name__, 'templates') return [resource_dir] def get_preference_panels(self, req): yield ('announcer', _('Announcements')) yield ('exp-announcer', 'Exp.Announcements') def _get_boxes(self, req): for pr in self.preference_boxes: boxes = pr.get_announcement_preference_boxes(req) boxdata = {} if boxes: for boxname, boxlabel in boxes: if boxname == 'general_wiki' and not req.perm.has_permission('WIKI_VIEW'): continue if (boxname == 'legacy' or boxname == 'joinable_groups') and not req.perm.has_permission('TICKET_VIEW'): continue yield ((boxname, boxlabel) + pr.render_announcement_preference_box(req, boxname)) def render_preference_panel(self, req, panel, path_info=None): streams = [] chrome = Chrome(self.env) for name, label, template, data in self._get_boxes(req): streams.append((label, chrome.render_template( req, template, data, content_type='text/html', fragment=True ))) add_stylesheet(req, 'announcer/css/announcer_prefs.css') return 'prefs_announcer.html', {"boxes": streams}

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124

0.675966

457

3,907

5.667396

0.455142

0.012355

0.013127

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0

0.004987

0.2301

3,907

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125

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0.432045

0

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0.015096

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false

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0.44186

0

null

0

null

0

1

0

fabea44e446c06b8ed7bd4cbe6c687ec876e43a7

5,946

py

Python

sher_scrapper.py

iammhk/AI_Poet

7676cd5b7740925857a4dd4923755e66c62e49f5

[ "Apache-2.0" ]

null

sher_scrapper.py

iammhk/AI_Poet

7676cd5b7740925857a4dd4923755e66c62e49f5

[ "Apache-2.0" ]

null

sher_scrapper.py

iammhk/AI_Poet

7676cd5b7740925857a4dd4923755e66c62e49f5

[ "Apache-2.0" ]

null

import requests from bs4 import BeautifulSoup global json_content import sqlite3 conn = sqlite3.connect("khusrau.db") cur = conn.cursor() blues_words=["","",""] tag_strip=['wafa', 'Wahm', 'Wahshat', 'Waiz', 'Wajood', 'Waqt', 'Welcome', 'Yaad', 'Yaad-e-Raftagan', 'Zindagi', 'zindan', 'Zulf'] top100=['https://rekhta.org/tags/wafa-shayari', 'https://rekhta.org/tags/wahm-shayari', 'https://rekhta.org/tags/wahshat-shayari', 'https://rekhta.org/tags/waiz-shayari', 'https://rekhta.org/tags/wajood-shayari', 'https://rekhta.org/tags/waqt-shayari', 'https://rekhta.org/tags/welcome-shayari', 'https://rekhta.org/tags/yaad-shayari', 'https://rekhta.org/tags/yaad-e-raftagan-shayari', 'https://rekhta.org/tags/zindagi-shayari', 'https://rekhta.org/tags/zindan-shayari', 'https://rekhta.org/tags/zulf-shayari'] url="https://rekhta.org/Top-20-Ishq-Sher" for m in range(0,len(top100)): net_url=url+top100[m] page = requests.get(top100[m]) soup = BeautifulSoup(page.content, 'lxml') #tree = html.fromstring(page.content) meaning_api="https://rekhta.org/Api_ShowMeaning/?id=" x=0 poem_title=soup.find("title").get_text() print(poem_title) #poem_author = soup.find(class_="ghazalAuthor") #print(poem_author.get_text()) word_id=[] desc=[] word_meaning="" sentence1="" sentence0="" trans="" trans_trim="" list1="" list0="" tags_net="" poem_raw = soup.find(class_="left_pan pageContentContainer") poem_couplet = poem_raw.find_all(class_=" nw_ghazalCard") #cur.execute("INSERT INTO ghazal VALUES (?, ?, ?, ?, ?, ?);", (poem_title,poem_author.get_text(),net_url,None,None,None)) #cur.execute("INSERT INTO author VALUES (?, ?, ?, ?, ?);", (poem_author.get_text(), None, None, None, None)) for y in range(0,len(poem_couplet)): #finds sher print(y) tag_container = poem_couplet[y].find(class_="tagContainingList") if tag_container is not None: tags = tag_container.find_all("li") else: tags=["yo"] lower=poem_couplet[y].find(class_="OptContainingList") blues=lower.find_all(class_="skyblue") full_g=lower.find(class_="ReadFull") for z in range (0,len(blues)): blues_words=blues[0].get_text() #print(blues_words) if blues_words == 'TRANSLATION': blues.pop(0) print(blues[0].get_text()) author_id=blues[0].get_text() full_ghazal="https://rekhta.org"+blues[-1]["href"] print(full_ghazal) couplet = poem_couplet[y] poem_line = couplet.find(class_="PContainer") for q in range(1, len(tags)): tag_sing=tags[q].get_text() #tag_length = len(tag_single) #print(tag_single[-3]) if tag_sing[-3] is ",": tag_list=list(tag_sing) tag_list[-3]="" tag_s = ''.join(tag_list) tag_single = tag_s.strip() else: tag_single=tag_sing tags_net+= tag_single + " + " tags_net += tag_strip[m] print(tags_net) #print(poem_line) #for g in range(0,len(poem_line)): line = poem_line.find_all(class_="DivLine") #print(line) for h in range(0, len(line)): #finds line single_line=line[h] #print(h) poem_text = single_line.find_all(class_="WM") for x in range(0, len(poem_text)): #finds words poem_word=poem_text[x] word=poem_word.get_text() word_id=poem_word['data-key'] meaning_url= meaning_api + word_id + "&lang=0" #print(meaning_url) meaning_page = requests.get(meaning_url) meaning_soup = BeautifulSoup(meaning_page.content, 'lxml') meaning_panel= meaning_soup.find(class_="MeaningBoxWrap") #print(meaning_panel) meaning_lang= meaning_panel.find_all('li') #print(meaning_lang[2].get_text()) word_meaning+= meaning_lang[0].get_text() + " | " +meaning_lang[1].get_text() + " | "+ meaning_lang[2].get_text() #print(word_meaning) if h is 0: sentence0 += word sentence0 += " " list0 += word_id list0 += " " else: sentence1+=word sentence1+=" " list1 += word_id list1 += " " #print(word, end=' ') cur.execute("INSERT INTO words VALUES (?, ?, ?);", (word_id, word, word_meaning)) # word inserter word_meaning = "" #conn.commit() #cur.execute("INSERT INTO sher2ghazal VALUES (?, ?, ?);", (poem_title,y,sentence0)) #sher2ghazal inserter trans_couplet = couplet.find(class_="DivLineSmall PoemImageHost ImageTranslationHost") if (trans_couplet is not None): trans = trans_couplet.contents[0] + " + " + trans_couplet.contents[-1] trans_trim=trans.strip() print(sentence0) print(top100[m]) print(sentence1) #print(trans_couplet.contents[2]) cur.execute("INSERT INTO t20_sher VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?);",(sentence0, sentence0, list0, sentence1, list1, author_id, tags_net, full_ghazal, trans_trim, y, None)) #sher inserter sentence1 = "" sentence0 = "" trans = "" trans_trim = "" tags_net = "" list0 = "" list1 = "" #page=None conn.commit() conn.close()

46.093023

512

0.546418

676

5,946

4.602071

0.210059

0.053038

0.067502

0.069431

0.15172

0.018644

0

0.017714

0.306929

5,946

128

513

46.453125

0.7372

0.134544

0

0.15534

0

0.193348

0

1

0

false

0

0.029126

0

0.029126

0.07767

0

null

0

null

0

1

0

fabec2036d476b356cb17a4315ed1f417bd3a26e

2,818

py

Python

portfolio_api/db/user/operations.py

kkiyama117/fastAPI_template

b56b3184d7217bc33aabc1c1a40174e06c80e2f3

[ "MIT" ]

null

portfolio_api/db/user/operations.py

kkiyama117/fastAPI_template

b56b3184d7217bc33aabc1c1a40174e06c80e2f3

[ "MIT" ]

null

portfolio_api/db/user/operations.py

kkiyama117/fastAPI_template

b56b3184d7217bc33aabc1c1a40174e06c80e2f3

[ "MIT" ]

null

import sqlite3 from typing import List from sqlalchemy import Table from portfolio_api.domains import user from portfolio_api import exceptions from .schema import UserSchema from .. import connection async def get_users() -> List[user.UserGet]: users = UserSchema().get_table() query = users.select() return await connection.fetch_all(query) async def get_user(user_id: int) -> user.UserGet: """Get User domain by id. Args: user_id (): id of user Returns: user.UserGet: user domain """ users: Table = UserSchema().get_table() query = users.select().where(users.columns.id == user_id).limit(1) return await connection.fetch_one(query) async def get_user_by_email(email: str) -> user.UserGet: """Get User domain by id. Args: email(str): email of user Returns: user.UserGet: user domain """ users: Table = UserSchema().get_table() query = users.select().where(users.columns.email == email).limit(1) return await connection.fetch_one(query) async def create_user(user_data: user.UserCreate) -> user.UserGet: users: Table = UserSchema().get_table() query = users.insert().values( email=user_data.email, first_name=user_data.first_name, last_name=user_data.last_name, is_active=user_data.is_active, is_admin=False, ) try: record_id = await connection.execute(query) return await get_user(record_id) except sqlite3.IntegrityError: raise exceptions.UserAlreadyExistException(f"{user_data.email} data is already exists") except Exception as e: raise exceptions.DatabaseException(str(e)) async def update_user(user_id: int, user_data: user.UserUpdate) -> user.UserGet: users: Table = UserSchema().get_table() query = ( users.update() .where(users.columns.id == user_id) .values( email=user_data.email, first_name=user_data.first_name, last_name=user_data.last_name, is_active=user_data.is_active, is_admin=False, ) ) try: result = await connection.execute(query) if not result: raise exceptions.UserNotExistException(f"user_{user_id} does not exist") return await get_user(user_id) except sqlite3.IntegrityError as e: raise exceptions.BadRequestException(str(e)) async def delete_user(user_id: int): users: Table = UserSchema().get_table() query = users.delete().where(users.columns.id == user_id) try: return await connection.execute(query) except sqlite3.IntegrityError: raise exceptions.UserNotExistException(f"user_{user_id} does not exist") except Exception as e: raise exceptions.DatabaseException(str(e))

29.663158

95

0.671398

359

2,818

5.103064

0.211699

0.048035

0.058952

0.075328

0.628821

0.562227

0.512009

0.491266

0.456332

0.343886

0

0.002757

0.227821

2,818

94

96

29.978723

0.839154

0

0.4375

0

0.038162

0

1

0

false

0

0.109375

0

0.203125

0

null

0

null

0

1

0

fabef270eb13994f8a08403379a335113eae92a3

657

py

Python

test/test_engine.py

0xflotus/cutecharts

1e68616481099b89c777104fc8bd00518165a487

[ "MIT" ]

1

2019-10-14T02:55:27.000Z

test/test_engine.py

XksA-me/cutecharts

844a8910d6a96e3f2e6c688a2c350e763c3d394d

[ "MIT" ]

null

test/test_engine.py

XksA-me/cutecharts

844a8910d6a96e3f2e6c688a2c350e763c3d394d

[ "MIT" ]

null

from nose.tools import assert_equal, assert_in from cutecharts.charts.basic import BasicChart from cutecharts.faker import Faker from cutecharts.globals import AssetsHost def test_engine_render(): basic = BasicChart() html = basic.render() assert_in(AssetsHost.DEFAULT_HOST, html) assert_in("chartXkcd", html) def test_engine_render_notebook(): basic = BasicChart() html = basic.render_notebook().__html__() assert_in(AssetsHost.DEFAULT_HOST, html) assert_in("chartXkcd", html) def test_faker(): attrs = Faker.choose() values = Faker.values() assert_equal(len(attrs), len(values))

25.269231

47

0.707763

80

657

5.5625

0.3375

0.089888

0.080899

0.085393

0.408989

0.274157

0

0.193303

657

25

48

26.28

0.839623

0

0.333333

0

0.028481

0

0.333333

1

0.166667

false

0

0.222222

0

0.388889

0

null

0

null

0

1

0

fac10195498b6e1b77a6635ec6e48a1790c2cc27

1,532

py

Python

recognise_in_live_video.py

mrdarwin4921/Face-Detection-

151532db85b70f97192421349e629ad9c548d302

[ "Apache-2.0" ]

1

2020-10-01T07:57:31.000Z

recognise_in_live_video.py

mrdarwin4921/Face-Detection-

151532db85b70f97192421349e629ad9c548d302

[ "Apache-2.0" ]

null

recognise_in_live_video.py

mrdarwin4921/Face-Detection-

151532db85b70f97192421349e629ad9c548d302

[ "Apache-2.0" ]

1

2020-10-01T06:02:24.000Z

from face_normalisation import get_normalised_faces from train_model import * import cv2 cap = cv2.VideoCapture(0) faceCascade = cv2.CascadeClassifier('P:\\GIT_FILE\\Face_Recognition\\OpenCVDemo\\haarcascade_frontalface_default.xml') while True: ret, frame = cap.read() gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) face_coord = faceCascade.detectMultiScale(gray, scaleFactor=1.2, minNeighbors=5) if len(face_coord): faces = get_normalised_faces(gray, face_coord) for i, face in enumerate(faces): pred, conf = rec_fisher.predict(face) image, labels, labels_dict = collect_dataset() threshold = 1000 if conf < threshold: per = int((threshold - conf) / threshold * 100) cv2.putText(frame, labels_dict[pred].capitalize() + str(per), (face_coord[i][0], face_coord[i][1] - 10), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 1) else: cv2.putText(frame, "Unknown", (face_coord[i][0], face_coord[i][1] - 10), cv2.FONT_HERSHEY_DUPLEX, 1, (0, 255, 0), 1) cv2.rectangle(frame, (face_coord[i][0], face_coord[i][1]), (face_coord[i][0] + face_coord[i][2], face_coord[i][1] + face_coord[i][3]), (255, 0, 0), 1) cv2.imshow('frame', frame) k = cv2.waitKey(30) & 0xff if k==27: break cap.release() cv2.destroyAllWindows()

38.3

163

0.575718

191

1,532

4.450262

0.434555

0.137647

0.117647

0.051765

0.181176

0.164706

0.115294

0.089412

0

0.05915

0.293734

1,532

39

164

39.282051

0.726433

0

0.064516

0

0.060951

0.052914

0

0.002679

0

1

0

false

0

0.096774

0

0.096774

0

null

0

null

0

1

0

fac16197f12780b2bd3a10bf8f76be34cd94385f

721

py

Python

main.py

ZhouBo20171229/-

56c9d859d6931cd971419c5225199acca6c189e5

[ "MIT" ]

null

main.py

ZhouBo20171229/-

56c9d859d6931cd971419c5225199acca6c189e5

[ "MIT" ]

null

main.py

ZhouBo20171229/-

56c9d859d6931cd971419c5225199acca6c189e5

[ "MIT" ]

null

from RoiMatching import * def Roimatching(RoiZipPath1, RoiZipPath2): # [Dic1, DirPath1] = DicBuild('C:\Result\JR1.zip') # [Dic2, DirPath2] = DicBuild('C:\Result\JR4.zip') [Dic1, DirPath1] = DicBuild(RoiZipPath1) [Dic2, DirPath2] = DicBuild(RoiZipPath2) Rename((Match(Dic1, Dic2))[0], DirPath1) Rename((Match(Dic1, Dic2))[1], DirPath2) if __name__ == '__main__': # print(os.path.dirname(os.path.realpath(__file__))) CurrentProjectPath = os.path.dirname(os.path.realpath(__file__)) RoiZipPath1 = os.path.join(CurrentProjectPath, 'test1.zip') RoiZipPath2 = os.path.join(CurrentProjectPath, 'test2.zip') Roimatching(RoiZipPath1, RoiZipPath2)#入参均为roi的.zip文件

36.05

69

0.682386

79

721

6.025316

0.443038

0.07563

0.138655

0.079832

0.130252

0

0.046434

0.163662

721

19

70

37.947368

0.742952

0.224688

0

0.048689

0

1

0.090909

false

0

0.090909

0

0.181818

0

null

0

null

0

1

0

fac17ff2df44015d75bec1cba8ddd57067048691

861

py

Python

Python/lc_695_max_area_of_island.py

cmattey/leetcode_problems

fe57e668db23f7c480835c0a10f363d718fbaefd

[ "MIT" ]

6

2019-07-01T22:03:25.000Z

2020-04-06T15:17:46.000Z

Python/lc_695_max_area_of_island.py

cmattey/leetcode_problems

fe57e668db23f7c480835c0a10f363d718fbaefd

[ "MIT" ]

null

Python/lc_695_max_area_of_island.py

cmattey/leetcode_problems

fe57e668db23f7c480835c0a10f363d718fbaefd

[ "MIT" ]

1

2020-04-01T22:31:41.000Z

# 695. Max Area of Island # Time: O(size(grid)) # Space: O(1) except recursion stack, since modifying grid in-place else O(size(grid)) class Solution: def maxAreaOfIsland(self, grid: List[List[int]]) -> int: max_count = 0 for row in range(len(grid)): for col in range(len(grid[0])): if grid[row][col]==1: count = self.dfs(grid, row, col) max_count = max(max_count, count) return max_count def dfs(self, grid, row, col): if row not in range(len(grid)) or col not in range(len(grid[0])): return 0 if grid[row][col]==1: grid[row][col]='#' return 1 + self.dfs(grid, row+1, col) + self.dfs(grid, row, col+1) + self.dfs(grid, row-1, col) + self.dfs(grid, row, col-1) else: return 0

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false

0

0.411765

0

null

0

null

0

1

0

fac57ace8db8b61ce13a568229153f9e64dbb9ce

1,293

py

Python

marshmallow_pagination/pages.py

fecgov/marshmallow-pagination

3180169f93680ee5e07baf97d28d0913d9a3f036

[ "MIT" ]

5

2016-02-15T19:51:53.000Z

2020-07-02T16:25:07.000Z

marshmallow_pagination/pages.py

jmcarp/marshmallow-pagination

626a6a97e71874565d453286ec211662ee226335

[ "MIT" ]

3

2015-11-29T01:23:59.000Z

2019-10-18T20:32:03.000Z

marshmallow_pagination/pages.py

fecgov/marshmallow-pagination

3180169f93680ee5e07baf97d28d0913d9a3f036

[ "MIT" ]

4

2015-08-31T04:23:09.000Z

2020-10-02T08:52:38.000Z

# -*- coding: utf-8 -*- import abc import collections import six class BasePage(six.with_metaclass(abc.ABCMeta, collections.Sequence)): """A page of results. """ def __init__(self, paginator, results): self.paginator = paginator self.results = results def __len__(self): return len(self.results) def __getitem__(self, index): return self.results[index] @abc.abstractproperty def info(self): pass class OffsetPage(BasePage): def __init__(self, paginator, page, results): self.page = page super(OffsetPage, self).__init__(paginator, results) @property def info(self): return { 'page': self.page, 'count': self.paginator.count, 'pages': self.paginator.pages, 'per_page': self.paginator.per_page, } class SeekPage(BasePage): @property def last_indexes(self): if self.results: return self.paginator._get_index_values(self.results[-1]) return None @property def info(self): return { 'count': self.paginator.count, 'pages': self.paginator.pages, 'per_page': self.paginator.per_page, 'last_indexes': self.last_indexes, }

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0.5

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null

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fac6a2160ecfd9f8b512cd53db2ca3d7435549a9

11,805

py

Python

venv/lib/python2.7/site-packages/ansible/modules/storage/netapp/na_ontap_snapshot.py

haind27/test01

7f86c0a33eb0874a6c3f5ff9a923fd0cfc8ef852

[ "MIT" ]

37

2017-08-15T15:02:43.000Z

2021-07-23T03:44:31.000Z

venv/lib/python2.7/site-packages/ansible/modules/storage/netapp/na_ontap_snapshot.py

haind27/test01

7f86c0a33eb0874a6c3f5ff9a923fd0cfc8ef852

[ "MIT" ]

12

2018-01-10T05:25:25.000Z

2021-11-28T06:55:48.000Z

venv/lib/python2.7/site-packages/ansible/modules/storage/netapp/na_ontap_snapshot.py

haind27/test01

7f86c0a33eb0874a6c3f5ff9a923fd0cfc8ef852

[ "MIT" ]

49

2017-08-15T09:52:13.000Z

2022-03-21T17:11:54.000Z

#!/usr/bin/python # (c) 2018, NetApp, Inc # GNU General Public License v3.0+ # (see COPYING or https://www.gnu.org/licenses/gpl-3.0.txt) from __future__ import absolute_import, division, print_function __metaclass__ = type ANSIBLE_METADATA = {'metadata_version': '1.1', 'status': ['preview'], 'supported_by': 'community'} DOCUMENTATION = ''' module: na_ontap_snapshot short_description: Manage NetApp Sanpshots extends_documentation_fragment: - netapp.na_ontap version_added: '2.6' author: - Chris Archibald (carchi@netapp.com), Kevin Hutton (khutton@netapp.com) description: - Create/Modify/Delete Ontap snapshots options: state: description: - If you want to create/modify a snapshot, or delete it. choices: ['present', 'absent'] default: present snapshot: description: Name of the snapshot to be managed. The maximum string length is 256 characters. required: true volume: description: - Name of the volume on which the snapshot is to be created. required: true async_bool: description: - If true, the snapshot is to be created asynchronously. type: bool comment: description: A human readable comment attached with the snapshot. The size of the comment can be at most 255 characters. snapmirror_label: description: A human readable SnapMirror Label attached with the snapshot. Size of the label can be at most 31 characters. ignore_owners: description: - if this field is true, snapshot will be deleted even if some other processes are accessing it. type: bool snapshot_instance_uuid: description: - The 128 bit unique snapshot identifier expressed in the form of UUID. vserver: description: - The Vserver name new_comment: description: A human readable comment attached with the snapshot. The size of the comment can be at most 255 characters. This will replace the existing comment ''' EXAMPLES = """ - name: create SnapShot tags: - create na_ontap_snapshot: state=present snapshot={{ snapshot name }} volume={{ vol name }} comment="i am a comment" vserver={{ vserver name }} username={{ netapp username }} password={{ netapp password }} hostname={{ netapp hostname }} - name: delete SnapShot tags: - delete na_ontap_snapshot: state=absent snapshot={{ snapshot name }} volume={{ vol name }} vserver={{ vserver name }} username={{ netapp username }} password={{ netapp password }} hostname={{ netapp hostname }} - name: modify SnapShot tags: - modify na_ontap_snapshot: state=present snapshot={{ snapshot name }} new_comment="New comments are great" volume={{ vol name }} vserver={{ vserver name }} username={{ netapp username }} password={{ netapp password }} hostname={{ netapp hostname }} """ RETURN = """ """ import traceback from ansible.module_utils.basic import AnsibleModule from ansible.module_utils._text import to_native import ansible.module_utils.netapp as netapp_utils HAS_NETAPP_LIB = netapp_utils.has_netapp_lib() class NetAppOntapSnapshot(object): """ Creates, modifies, and deletes a Snapshot """ def __init__(self): self.argument_spec = netapp_utils.na_ontap_host_argument_spec() self.argument_spec.update(dict( state=dict(required=False, choices=[ 'present', 'absent'], default='present'), snapshot=dict(required=True, type="str"), volume=dict(required=True, type="str"), async_bool=dict(required=False, type="bool", default=False), comment=dict(required=False, type="str"), snapmirror_label=dict(required=False, type="str"), ignore_owners=dict(required=False, type="bool", default=False), snapshot_instance_uuid=dict(required=False, type="str"), vserver=dict(required=True, type="str"), new_comment=dict(required=False, type="str"), )) self.module = AnsibleModule( argument_spec=self.argument_spec, supports_check_mode=True ) parameters = self.module.params # set up state variables # These are the required variables self.state = parameters['state'] self.snapshot = parameters['snapshot'] self.vserver = parameters['vserver'] # these are the optional variables for creating a snapshot self.volume = parameters['volume'] self.async_bool = parameters['async_bool'] self.comment = parameters['comment'] self.snapmirror_label = parameters['snapmirror_label'] # these are the optional variables for deleting a snapshot\ self.ignore_owners = parameters['ignore_owners'] self.snapshot_instance_uuid = parameters['snapshot_instance_uuid'] # These are the optional for Modify. # You can NOT change a snapcenter name self.new_comment = parameters['new_comment'] if HAS_NETAPP_LIB is False: self.module.fail_json( msg="the python NetApp-Lib module is required") else: self.server = netapp_utils.setup_na_ontap_zapi( module=self.module, vserver=self.vserver) return def create_snapshot(self): """ Creates a new snapshot """ snapshot_obj = netapp_utils.zapi.NaElement("snapshot-create") # set up required variables to create a snapshot snapshot_obj.add_new_child("snapshot", self.snapshot) snapshot_obj.add_new_child("volume", self.volume) # Set up optional variables to create a snapshot if self.async_bool: snapshot_obj.add_new_child("async", self.async_bool) if self.comment: snapshot_obj.add_new_child("comment", self.comment) if self.snapmirror_label: snapshot_obj.add_new_child( "snapmirror-label", self.snapmirror_label) try: self.server.invoke_successfully(snapshot_obj, True) except netapp_utils.zapi.NaApiError as error: self.module.fail_json(msg='Error creating snapshot %s: %s' % (self.snapshot, to_native(error)), exception=traceback.format_exc()) def delete_snapshot(self): """ Deletes an existing snapshot """ snapshot_obj = netapp_utils.zapi.NaElement("snapshot-delete") # Set up required variables to delete a snapshot snapshot_obj.add_new_child("snapshot", self.snapshot) snapshot_obj.add_new_child("volume", self.volume) # set up optional variables to delete a snapshot if self.ignore_owners: snapshot_obj.add_new_child("ignore-owners", self.ignore_owners) if self.snapshot_instance_uuid: snapshot_obj.add_new_child( "snapshot-instance-uuid", self.snapshot_instance_uuid) try: self.server.invoke_successfully(snapshot_obj, True) except netapp_utils.zapi.NaApiError as error: self.module.fail_json(msg='Error deleting snapshot %s: %s' % (self.snapshot, to_native(error)), exception=traceback.format_exc()) def modify_snapshot(self): """ Modify an existing snapshot :return: """ snapshot_obj = netapp_utils.zapi.NaElement("snapshot-modify-iter") # Create query object, this is the existing object query = netapp_utils.zapi.NaElement("query") snapshot_info_obj = netapp_utils.zapi.NaElement("snapshot-info") snapshot_info_obj.add_new_child("name", self.snapshot) query.add_child_elem(snapshot_info_obj) snapshot_obj.add_child_elem(query) # this is what we want to modify in the snapshot object attributes = netapp_utils.zapi.NaElement("attributes") snapshot_info_obj = netapp_utils.zapi.NaElement("snapshot-info") snapshot_info_obj.add_new_child("name", self.snapshot) snapshot_info_obj.add_new_child("comment", self.new_comment) attributes.add_child_elem(snapshot_info_obj) snapshot_obj.add_child_elem(attributes) try: self.server.invoke_successfully(snapshot_obj, True) except netapp_utils.zapi.NaApiError as error: self.module.fail_json(msg='Error modifying snapshot %s: %s' % (self.snapshot, to_native(error)), exception=traceback.format_exc()) def does_snapshot_exist(self): """ Checks to see if a snapshot exists or not :return: Return True if a snapshot exists, false if it dosn't """ snapshot_obj = netapp_utils.zapi.NaElement("snapshot-get-iter") desired_attr = netapp_utils.zapi.NaElement("desired-attributes") snapshot_info = netapp_utils.zapi.NaElement('snapshot-info') comment = netapp_utils.zapi.NaElement('comment') # add more desired attributes that are allowed to be modified snapshot_info.add_child_elem(comment) desired_attr.add_child_elem(snapshot_info) snapshot_obj.add_child_elem(desired_attr) # compose query query = netapp_utils.zapi.NaElement("query") snapshot_info_obj = netapp_utils.zapi.NaElement("snapshot-info") snapshot_info_obj.add_new_child("name", self.snapshot) snapshot_info_obj.add_new_child("volume", self.volume) query.add_child_elem(snapshot_info_obj) snapshot_obj.add_child_elem(query) result = self.server.invoke_successfully(snapshot_obj, True) return_value = None # TODO: Snapshot with the same name will mess this up, # need to fix that later if result.get_child_by_name('num-records') and \ int(result.get_child_content('num-records')) == 1: attributes_list = result.get_child_by_name('attributes-list') snap_info = attributes_list.get_child_by_name('snapshot-info') return_value = {'comment': snap_info.get_child_content('comment')} return return_value def apply(self): """ Check to see which play we should run """ changed = False comment_changed = False netapp_utils.ems_log_event("na_ontap_snapshot", self.server) existing_snapshot = self.does_snapshot_exist() if existing_snapshot is not None: if self.state == 'absent': changed = True elif self.state == 'present' and self.new_comment: if existing_snapshot['comment'] != self.new_comment: comment_changed = True changed = True else: if self.state == 'present': changed = True if changed: if self.module.check_mode: pass else: if self.state == 'present': if not existing_snapshot: self.create_snapshot() elif comment_changed: self.modify_snapshot() elif self.state == 'absent': if existing_snapshot: self.delete_snapshot() self.module.exit_json(changed=changed) def main(): """ Creates, modifies, and deletes a Snapshot """ obj = NetAppOntapSnapshot() obj.apply() if __name__ == '__main__': main()

36.890625

78

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11,805

5.269457

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0.03344

0.027309

0.449352

0.374808

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0.253031

0

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0.004049

0

null

0

null

0

1

0

fac822218bfb438f9537b18947c9a3111177db0b

81,054

py

Python

cloudbackup/client/agents.py

rackerlabs/cbu-sdk-python

76647cdb2be19310f173b49a38e6a9b8077ee97d

[ "Apache-2.0" ]

4

2015-02-10T14:28:12.000Z

2016-12-26T22:52:07.000Z

cloudbackup/client/agents.py

rackerlabs/python-cloudbackup-sdk

76647cdb2be19310f173b49a38e6a9b8077ee97d

[ "Apache-2.0" ]

17

2015-01-22T21:58:36.000Z

2018-01-25T19:47:43.000Z

cloudbackup/client/agents.py

rackerlabs/cbu-sdk-python

76647cdb2be19310f173b49a38e6a9b8077ee97d

[ "Apache-2.0" ]

9

2015-01-26T19:25:45.000Z

2018-11-01T20:14:12.000Z

""" Rackspace Cloud Backup Agent API """ from __future__ import print_function import datetime import gzip import hashlib import json import logging import os import requests import time import threading import six from cloudbackup.common.command import Command requests.packages.urllib3.disable_warnings() class ParameterError(Exception): """ Parameter Error Exception """ pass # function for Agents class to use to keep a given agent awake def _keep_agent_awake_thread_fn(my_notifier=None, userid=None, usertype=None, credentials=None, method=None, rse_app=None, rse_version=None, rse_agentkey=None, rse_log=None, rse_apihost=None, rse_period=None, apihost=None, agent_id=None, api_version=None, project_id=None): """ (Internal) Thread function that will periodically post the wake agent message and look for the specified agent Aside from my_notifier, the function maintains its own objects internally in thread local data storage for thread-safety purposes Require parameters: my_notifier - threading.Event object instance that signals thread termination userid - username for Keystone/Identity authentication usertype - user type see cloudbackup.client.auth.Authentication for details credentials - apikey for Keystone/Identity authentication method - authentication method see cloudbackup.client.auth.Authentication for details rse_app - RSE Application Name rse_version - RSE Application Version rse_agentkey - RSE Channel to listen to rse_period - period between wake agent calls apihost - Rackspace Cloud Backup API URL api_verison - Rackspace Cloud Backup API version agent_id - machine agent identifier for the agent to monitor for project_id - for Rackspace Cloud Backup API version 2 and newer the tenantid (aka project_id) is required Option parameters: rse_log - Base log file name, the thread will append data to create a unique RSE log file name for the thread's RSE queries. If not desired, specify None rse_apihost - RSE API URL See cloudbackup.clients.rse.Rse for details """ if None in (my_notifier, userid, usertype, credentials, method, rse_app, rse_version, rse_agentkey, rse_period, apihost, agent_id, api_version): msg_missing = [] if my_notifier is None: msg_missing.append('my_notifier') if userid is None: msg_missing.append('userid') if usertype is None: msg_missing.append('usertype') if credentials is None: msg_missing.append('credentials') if method is None: msg_missing.append('method') if rse_app is None: msg_missing.append('rse_app') if rse_version is None: msg_missing.append('rse_version') if rse_agentkey is None: msg_missing.append('rse_agentkey') if rse_period is None: msg_missing.append('rse_period') if apihost is None: msg_missing.append('apihost') if agent_id is None: msg_missing.append('agent_id') if api_version is None: msg_missing.append('api_version') raise RuntimeError('Invalid parameters. Some required parameters were not properly specified. Missing parameters: {0}'.format(msg_missing)) if api_version > 1: if project_id is None: raise RuntimeError('Invalid parameters. api_version = {0} and project_id is missing.' .format(api_version)) log = logging.getLogger(__name__) # For threading simplicity we are going to create thread local version of each of the required objects import cloudbackup.client.auth import cloudbackup.client.rse data = threading.local() data.thread_id = threading.current_thread().ident data.log_prefix = 'RSE Wakeup Thread[{0:}] Log'.format(data.thread_id) data.auth_engine = cloudbackup.client.auth.Authentication( userid, credentials, usertype=usertype, method=method ) data.agent_engine = cloudbackup.client.agents.Agents(True, data.auth_engine, apihost, api_version, project_id) data.logfile = None if rse_log is not None: data.logfile = '{0:}.thread_{1:}'.format(rse_log, data.thread_id) log.debug('{0:}: {1:}'.format(data.log_prefix, data.logfile)) log.debug('{0:}: Agent Id - {1:}'.format(data.log_prefix, agent_id)) log.debug('{0:}: RSE Period - {1:}'.format(data.log_prefix, rse_period)) data.rse_engine = cloudbackup.client.rse.Rse(rse_app, rse_version, data.auth_engine, data.agent_engine, rse_agentkey, logfile=data.logfile, apihost=rse_apihost, api_version=api_version, project_id=project_id) def __check_notifier(notifier): """ Simple wrapper to check the notifier and return whether or not the loop should exit Parameters: notifier - threading.Event object instance Returns: True if the loop should continue (event is not set) False if the loop should terminate (event is set) """ if notifier.is_set(): notifier.clear() log.debug('{0:}: Detected termination.'.format(data.log_prefix)) return False return True # 10 second timeout rse_timeout = 10000 continue_loop = True while continue_loop: # Check the thread status before we try to wake the agent continue_loop = __check_notifier(my_notifier) if not continue_loop: break if data.agent_engine.WakeSpecificAgent(agent_id, data.rse_engine, rse_timeout): # Agent is awake, so wait for the period before checking again start_time = int(round(time.time() * 1000)) finish_time = start_time + rse_period while ((int(round(time.time() * 1000))) < finish_time) and continue_loop: # check the thread status every 1 second throughout the entire period wait continue_loop = __check_notifier(my_notifier) time.sleep(1) else: # Failed to wake the agent log.debug('{0:}: Failed to wake agent - {1:}'.format(data.log_prefix, agent_id)) log.debug('{0:}: Terminating'.format(data.log_prefix)) class AgentDetailsNotAvailable(Exception): """ Agent Details are not available """ pass class AgentConfigurationNotAvailable(Exception): """ Agent Configuraiton is not available """ pass class AgentLogLevel(Command): """ Object controlling the log levels for agents """ def __init__(self, sslenabled, authenticator, apihost, api_version=1, project_id=None): super(self.__class__, self).__init__(sslenabled, apihost, '/') self.log = logging.getLogger(__name__) # save the ssl status for the various reinits done for each API call supported self.sslenabled = sslenabled self.authenticator = authenticator self.loglevel = {} if type(api_version) is int: self.api_version = api_version else: self.api_version = 1 self.project_id = project_id def __del__(self): try: if len(self.loglevel): for machine_agent in self.loglevel.keys(): while self.HasLogLevels(machine_agent): self.PopLogLevel(machine_agent) except: pass def GetLogLevel(self, machine_agent_id): """ Retrieve the current log level for the agent from the API The returned value will be one of the following: Fatal Error Warn Info Debug Trace All """ if self.api_version == 1: self.ReInit(self.sslenabled, '/v1.0/agent/logging/{0}'.format(machine_agent_id)) self.headers['X-Auth-Token'] = self.authenticator.AuthToken self.headers['Content-Type'] = 'application/json; charset=utf-8' else: self.ReInit(self.sslenabled, '/v{0}/{1}/agents/{2}'.format(self.api_version, self.project_id, machine_agent_id)) self.headers['X-Auth-Token'] = self.authenticator.AuthToken self.headers['Content-Type'] = 'application/json; charset=utf-8' self.headers['X-Project-Id'] = self.project_id res = requests.get(self.Uri, headers=self.Headers) if res.status_code == 200: if self.api_version == 1: # the text will be data like "Warn" (with quotes) so remove the quotes. return res.text.replace('"', '') else: return res.json()['log_level'] else: self.log.error('Unable to retrieve agent log level for machine agent id ' + str(machine_agent_id) + '. Server returned ' + str(res.status_code) + ': ' + res.text + ' Reason: ' + res.reason) return '' def SetLogLevel(self, machine_agent_id, level): """ Set the log level for the agent via the API 'level' must be one of the following: Fatal Error Warn Info Debug Trace All 'level' may also be a numeric value inclusively between 1 and 7. """ if self.api_version == 1: self.log.info('v1 Set Log Level') if not level in ('Fatal', 'Error', 'Warn', 'Info', 'Debug', 'Trace', 'All', 1, 2, 3, 4, 5, 6, 7): raise ValueError('Log Level (' + str(level) + ') is not valid.') self.ReInit(self.sslenabled, "/v1.0/agent/logging") self.headers['X-Auth-Token'] = self.authenticator.AuthToken self.headers['Content-Type'] = 'application/json; charset=utf-8' o = {} o['MachineAgentId'] = machine_agent_id levels = { 'Fatal': 1, 'Error': 2, 'Warn': 3, 'Info': 4, 'Debug': 5, 'Trace': 6, 'All': 7 } if level in levels: o['LoggingLevelid'] = levels[level] else: o['LoggingLevelid'] = level self.body = json.dumps(o) res = requests.put(self.Uri, headers=self.Headers, data=self.Body) else: self.log.info('v{0} Set Log Level'.format(self.api_version)) # TODO: Need to rework this whole function self.ReInit(self.sslenabled, '/v{0}/{1}/agents/{2}'.format(self.api_version, self.project_id, machine_agent_id)) self.headers['X-Auth-Token'] = self.authenticator.AuthToken self.headers['Content-Type'] = 'application/json; charset=utf-8' self.headers['X-Project-Id'] = self.project_id o = {} o['op'] = 'replace' o['path'] = '/log_level' o['value'] = level.lower() l = [] l.append(o) self.body = json.dumps(l) self.log.debug('Updating Log Level: {0}'.format(o)) res = requests.patch(self.Uri, headers=self.Headers, data=self.Body) if res.status_code == 204: self.log.info('Updated log level to {0}'.format(level)) return True else: self.log.error('Unable to set the log level. Server returned ' + str(res.status_code) + ': ' + res.text + ' Reason: ' + res.reason) return False def PushLogLevel(self, machine_agent_id, level): """ Save the current log level and set 'level' as the new log level. See SetLogLevel() for valid values of 'level' Note: Log Levels are stored as a Stack. Use PopLogLevel() to restore the log level to the value prior to calling PushLogLevel(). """ if not machine_agent_id in self.loglevel: self.loglevel[machine_agent_id] = list() current = self.GetLogLevel(machine_agent_id) self.loglevel[machine_agent_id].append(current) self.SetLogLevel(machine_agent_id, level) def HasLogLevels(self, machine_agent_id): """ Returns whether or not there are any log levels for the given machine agent id """ if machine_agent_id in self.loglevel: if len(self.loglevel[machine_agent_id]): return True else: return False else: return False def PopLogLevel(self, machine_agent_id): """ Restore the previous log level if it exists. If not log level has been saved, then it does nothing. Note: Log Levels are stored as a Stack. Log Levels are added to the stack by calling PushLogLevel(). """ if machine_agent_id in self.loglevel: if len(self.loglevel[machine_agent_id]): index = len(self.loglevel[machine_agent_id]) - 1 level = self.loglevel[machine_agent_id][index] if self.SetLogLevel(machine_agent_id, level): self.loglevel[machine_agent_id].pop(index) self.log.info('Restored Machine Agent Id (' + str(machine_agent_id) + ') Log Level to ' + level) else: self.log.error('Error while resetting the log level for Machine Agent Id (' + str(machine_agent_id) + ') to ' + level) else: self.log.error('Machine Agent Id (' + str(machine_agent_id) + ') is already at the root log level. Nothing left to pop.') else: self.log.error('Machine Agent Id (' + str(machine_agent_id) + ') does not have any stacked log levels') class AgentDetails(object): """ Object describing a given Agent instance described by the Agent Details API Endpoint """ def __init__(self, details, version=1): # TODO: Replace this verification and use JSON Schema self.version = version if self.version == 1: # Verify the details are at least what we expect before doing anything else for prop in ('MachineAgentId', 'AgentVersion', 'Architecture', 'Flavor', 'BackupVaultSize', 'CleanupAllowed', 'Datacenter', 'IPAddress', 'IsDisabled', 'IsEncrypted', 'MachineName', 'OperatingSystem', 'OperatingSystemVersion', 'PublicKey', 'Status', 'TimeOfLastSuccessfulBackup', 'UseServiceNet', 'HostServerId'): x = details[prop] # TODO: Add JSON Schema validation for API v2 # Some cached data needed self._details = details @property def agent_id(self): """ Agent ID """ if self.version == 1: return self._details['MachineAgentId'] else: return self._details['id'] @property def AgentVersion(self): """ Agent Version """ if self.version == 1: return self._details['AgentVersion'] else: return self._details['version'] @property def Architecture(self): """ System Architecture """ if self.version == 1: return self._details['Architecture'] else: return self._details['host']['os']['architecture'] @property def Flavor(self): """ System Flavor """ if self.version == 1: return self._details['Flavor'] else: return self._details['host']['flavor'] @property def BackupVaultSize(self): """ Current size of the Backup Vault """ # TODO: v2 does not have Backup Vault Size return self._details['BackupVaultSize'] @property def CleanupAllowed(self): """ Can Cleanup the Vault? """ # TODO: v2 does not have CleanupAllowed return self._details['CleanupAllowed'] @property def Datacenter(self): """ Which Datacenter does the system live in? """ if self.version == 1: return self._details['Datacenter'] else: return self._details['host']['region'] @property def IPAddress(self): """ IP Address the agent registered with """ if self.version == 1: return self._details['IPAddress'] else: return next(address for address in self._details['host']['addresses'] if address['version'] == 4)['addr'] @property def IsDisabled(self): """ Is the Agent Disabled? """ if self.version == 1: return self._details['IsDisabled'] else: return not self._details['enabled'] @property def IsEnabled(self): """ Is the Agent Enabled? """ return not self.IsDisabled @property def IsEncrypted(self): """ Are the backups encrypted? """ if self.version == 1: return self._details['IsEncrypted'] else: return self._details['vault']['encrypted'] @property def MachineName(self): """ System Name as registered with Cloud Servers (Nova) """ if self.version == 1: return self._details['MachineName'] else: return self._details['name'] @property def OperatingSystem(self): """ System Operating System """ if self.version == 1: return self._details['OperatingSystem'] else: return self._details['host']['os']['name'] @property def OperatingSystemVersion(self): """ System Operating System Version """ if self.version == 1: return self._details['OperatingSystemVersion'] else: return self._details['host']['os']['version'] @property def PublicKey(self): """ Public Key for encrypted backups """ if self.version == 1: return self._details['PublicKey'] else: # TODO: Content of rsa_public_key is a different from that of PublicKey. Is this function being used? return self._details['rsa_public_key'] @property def Status(self): """ Agent Status """ if self.version == 1: return self._details['Status'] # TODO: API v2 provides http://docs.cloudbackupapi.apiary.io/#reference/agents/v2agentsidstatus/get-an-agent's-status # to get a real-time status of the agent @property def TimeOfLastSuccessfulBackup(self): """ When was the agent last succcessful with its backup? """ if self.version == 1: return self._details['TimeOfLastSuccessfulBackup'] # TODO: API v2 provides this a little differently: # First call http://docs.cloudbackupapi.apiary.io/#reference/configurations/v2configurationsid/get-details-about-a-configuration # to get the last time a given configuration was backed up, then retrieve the details via # http://docs.cloudbackupapi.apiary.io/#reference/backups/v2backupsid/get-details-about-a-backup to find the time of that backup @property def DateTimeOfLastSuccessfulBackup(self): """ When was the agent last succcessful with its backup? """ if (not self.TimeOfLastSuccessfulBackup): return None a = self.TimeOfLastSuccessfulBackup.split('(') b = a[1].split(')') unix_epoch = b[0] return datetime.datetime.utcfromtimestamp(float(unix_epoch) / 1000.0) @property def UseServiceNet(self): """ Use RAX ServiceNet? """ if self.version == 1: return self._details['UseServiceNet'] else: return self._details['vault']['use_internal'] @property def HostServerId(self): """ System Host Server Identifier for Cloud Servers (Nova) """ if self.version == 1: return self._details['HostServerId'] else: return self._details['host']['machine']['id'] class AgentConfiguration(object): """ Object describing the various Agent configurations """ def __init__(self, configuration, version=1): # TODO: Replace this verification and use JSON Schema self.version = version if self.version == 1: # Verify the configurations are at least what we expect before doing anything else for prop in ('Volumes', 'SystemPreferences', 'UserPreferences', 'BackupConfigurations'): x = configuration[prop] # TODO: Add JSON Schema validation for API v2 self.log = logging.getLogger(__name__) # some cached data needed self._configuration = configuration # Volumes[] # -> DataServices # -> Uri # -> FailoverUri # -> EncryptionEnabled # -> Password # -> NetworkDrives # -> BackupVaultId @property def Volumes(self): if self.version == 1: return self._configuration['Volumes'] else: # TODO: This is not a one to one mapping. The key/values are different return self._configuration['vaults'] # SystemPreferences See SystemPreferences # ->RateLimit # ->AutoUpdate # --> Enabled # --> LatestVersion # -> Environment # --> MinimumDiskSpaceMb # ---> Backup See MinimumBackupDiskSpaceMb() # ---> Restore See MinimumRestoreDiskSpaceMb() # ---> Cleanup See MinimumCleanupDiskSpaceMb() # -> Logging # --> Level See ConfigLogLevel() @property def SystemPreferences(self): if self.version == 1: return self._configuration['SystemPreferences'] else: # TODO: This is not a one to one mapping. The key/values are different return self._configuration['system_preferences'] @property def ConfigLogLevel(self): if self.version == 1: return self.SystemPreferences['Logging']['Level'] else: return self._configuration['system_preferences']['logging']['level'] @property def MinimumBackupDiskSpaceMb(self): if self.version == 1: return self.SystemPreferences['Environment']['MinimumDiskSpaceMb']['Backup'] else: return self._configuration['system_preferences']['environment']['minimum_disk_space_mb']['backup'] @property def MinimumRestoreDiskSpaceMb(self): if self.version == 1: return self.SystemPreferences['Environment']['MinimumDiskSpaceMb']['Restore'] else: return self._configuration['system_preferences']['environment']['minimum_disk_space_mb']['restore'] @property def MinimumCleanupDiskSpaceMb(self): if self.version == 1: return self.SystemPreferences['Environment']['MinimumDiskSpaceMb']['Cleanup'] else: return self._configuration['system_preferences']['environment']['minimum_disk_space_mb']['cleanup'] # UserPreferences # -> CacheDirectory # -> ThrottleBandwidth @property def UserPreferences(self): if self.version == 1: return self._configuration['UserPreferences'] else: # TODO: Need to look into the equivalent value/object return None # BackupConfigurations[] See GetBackupConfigurationById(), GetBackupConfigurationByName() # -> BackupPrescript # -> BackupPostscript # -> Id See GetBackupIds(), GetBackupIdNameMap() # -> VolumeUri # -> VolumeFailoverUri # -> Name See GetBackupNames(), GetBackupNameIdMap() # -> IsEnabled # -> DaysToKeepOldFileVersions # -> KeepOldFileVersionsIndefinitely # -> Schedules[] # --> Start # --> End # --> InitialScheduledTime # --> Frequency # --> TimeOfDay # --> DayOfWeek # --> HourlyInterval # --> IsDST # --> Offset # -> Inclusions[] # --> Pattern # --> Type # --> Module # --> Args # -> Exclusions[] # --> Pattern # --> Type # --> Module # --> Args @property def BackupConfigurations(self): if self.version == 1: return self._configuration['BackupConfigurations'] else: # TODO: This is not a one to one mapping. The key/values are different return self._configuration['configurations'] # Rse See GetRse() # -> Channel See GetRseChannel() # -> HostName See GetRseHost() # -> Polling See GetRsePollingConfig() # --> Interval # ---> Idle # ---> Active # ---> RealTime # --> Timeout # ---> Idle # ---> Active # ---> RealTime # -> Heartbeat See GetRseHeartbeatConfig() # --> Interval # ---> Idle # ---> Active # ---> RealTime # --> Timeout # ---> Idle # ---> Active # ---> RealTime @property def Rse(self): if self.version == 1: return self.SystemPreferences['Rse'] else: # TODO: This is not a one to one mapping. The key/values are different return self._configuration['system_preferences']['events']['rse'] @property def RseChannel(self): if self.version == 1: return self.Rse['Channel'] else: return self._configuration['system_preferences']['events']['rse']['channel'] @property def RseHost(self): if self.version == 1: return self.Rse['HostName'] else: return self._configuration['system_preferences']['events']['rse']['host'] @property def RsePollingConfig(self): if self.version == 1: return self.Rse['Polling'] else: return self._configuration['system_preferences']['events']['rse']['polling'] @property def RseHeartbeatConfig(self): if self.version == 1: return self.Rse['Heartbeat'] else: return self._configuration['system_preferences']['events']['rse']['heartbeat'] def GetBackupIds(self): """ Retrieve the list of Backup Configuration Ids for the agent as reported by GetAgentConfiguration() """ if self.version == 1: backup_id = 'Id' else: backup_id = 'id' backupids = set() for backupconfig in self.BackupConfigurations: backupids.add(backupconfig[backup_id]) return backupids def GetBackupNames(self): """ Retrieve the list of Backup Configuration Names for the agent as reported by GetAgentConfiguration() """ if self.version == 1: backup_name = 'Name' else: backup_name = 'name' backupnames = set() for backupconfig in self.BackupConfigurations: backupnames.add(backupconfig[backup_name]) return backupnames def GetBackupNameIdMap(self): """ Retrieve the list of Backup Configuration Names for the agent as reported by GetAgentConfiguration() """ if self.version == 1: backup_name = 'Name' backup_id = 'Id' else: backup_name = 'name' backup_id = 'id' backupnamemap = {} for backupconfig in self.BackupConfigurations: backupnamemap[backupconfig[backup_name]] = backupconfig[backup_id] return backupnamemap def GetBackupIdNameMap(self): """ Retrieve the list of Backup Configuration Names for the agent as reported by GetAgentConfiguration() """ if self.version == 1: backup_name = 'Name' backup_id = 'Id' else: backup_name = 'name' backup_id = 'id' backupidmap = {} for backupconfig in self.BackupConfigurations: backupidmap[backupconfig[backup_id]] = backupconfig[backup_name] return backupidmap def GetBackupIdFromName(self, backup_name): """ Translate the backup name into a backup id based on the agent data reported by GetAgentConfiguration() Note: It would be more performant to simply retrieve the configuration by the name instead of doing the translation """ backupnamemap = self.GetBackupNameIdMap() return backupnamemap[backup_name] def GetBackupNameFromId(self, backup_id): """ Translate the backup id into a backup name based on the agent data reported by GetAgentConfiguration() Note: It would be more performant to simply retrieve the configuration by the id instead of doing the translation """ backupidmap = self.GetBackupIdNameMap() return backupidmap[backup_id] def GetBackupConfigurationById(self, backup_id): """ Retrieve the entire backup configuration for the agent given a backup id, data as reported by GetAgentConfiguration() """ if self.version == 1: b_id = 'Id' else: b_id = 'id' return next((backupconfig for backupconfig in self.BackupConfigurations if backupconfig[b_id] == backup_id), {}) def GetBackupConfigurationByName(self, backup_name): """ Retrieve the entire backup configuration for the agent given a backup id, data as reported by GetAgentConfiguration() """ if self.version == 1: b_name = 'Name' else: b_name = 'name' return next((backupconfig for backupconfig in self.BackupConfigurations if backupconfig[b_name] == backup_name), {}) def GetVaultDbContainer(self, backup_name=None): """ Retrieve the URI for the VaultDB, data as reported by GetAgentConfiguration() """ if self.version == 1: container = None if backup_name is not None: backupconfig = self.GetBackupConfigurationByName(backup_name) container = backupconfig['VolumeUri'] else: container = self.Volumes[0]['Uri'] self.log.debug('VaultDB Container: ' + container) return container[6:] else: vault_info = self._configuration['vaults'][0] if vault_info['use_internal']: vault_url = next(url for url in vault_info['links'] if url['rel'] == 'internalURL')['href'] else: vault_url = next(url for url in vault_info['links'] if url['rel'] == 'publicURL')['href'] self.log.debug('VaultDB Container: ' + vault_url) # strip the https:// section return vault_url[8:] def GetVaultDbPath(self, backup_name=None): """ Retrieve the URI for the VaultDB, data as reported by GetAgentConfiguration() """ try: if self.version == 1: vaultvolume = {} if backup_name is not None: backupconfig = self.GetBackupConfigurationByName(backup_name) volumeuri = backupconfig['VolumeUri'] vaultvolume = {} # As there may be numerous volumes we match it up against the backup configuration we are looking for # Don't know if there is a better way or not...but this will work for now for volume in self.Volumes: if volume['Uri'] == volumeuri: vaultvolume = volume else: vaultvolume = self.Volumes[0] vaultdburi = 'BACKUPS/v2.0/' + vaultvolume['BackupVaultId'] self.log.debug('VaultDB Path: ' + vaultdburi) return vaultdburi else: backupconfig = self.GetBackupConfigurationByName(backup_name) vault_id = backupconfig['vault_id'] vaultdburi = 'BACKUPS/v2.0/{0}'.format(vault_id) self.log.debug('VaultDB Path: ' + vaultdburi) return vaultdburi except LookupError: self.log.error('Unable to access the Volume URI. Did GetAgentConfiguration get called first?') return '' def GetBundlePath(self, backup_name, bundle_id): """ Retrieve the URI for the Bundle Depends on GetAgentConfiguration() to have already been called """ try: if self.version == 1: backupconfig = self.GetBackupConfigurationByName(backup_name) volumeuri = backupconfig['VolumeUri'] vaultvolume = {} # As there may be numerous volumes we match it up against the backup configuration we are looking for # Don't know if there is a better way or not...but this will work for now for volume in self.Volumes: if volume['Uri'] == volumeuri: vaultvolume = volume vaultdburi = 'BACKUPS/v2.0/' + vaultvolume['BackupVaultId'] + '/BUNDLES/' + '{0:010}'.format(bundle_id) self.log.debug('VaultDB Path: ' + vaultdburi) return vaultdburi else: vault_db_url = self.GetVaultDbPath(backup_name) vaultdburi = vault_db_url + '/BUNDLES/' + \ '{0:010}'.format(bundle_id) self.log.debug('VaultDB Path: ' + vaultdburi) return vaultdburi except LookupError: self.log.error('Unable to access the Volume URI. Did GetAgentConfiguration get called first?') return '' class Agents(Command): """ Object defining HTTP REST API calls for interactiving with the Rackspace Cloud Backup Agent Presently supports the RAX v1.0 API """ def __init__(self, sslenabled, authenticator, apihost, api_version=1, project_id=None): """ Initialize the Agent access sslenabled - True if using HTTPS; otherwise False authenticator - instance of cloudbackup.client.auth.Authentication to use apihost - server to use for API calls api_version - version of the API project_id - Project Id used by API v2 """ super(self.__class__, self).__init__(sslenabled, apihost, '/') self.log = logging.getLogger(__name__) # save the ssl status for the various reinits done for each API call supported self.sslenabled = sslenabled self.authenticator = authenticator # Some cached data needed, set to invalid values by default self.agents = {} self.configurations = {} self.o = {} self.snapshot_id = -1 self.wake_agent_threads = [] self.loglevel = AgentLogLevel(sslenabled, authenticator, apihost, api_version, project_id) if type(api_version) is int: self.api_version = api_version else: self.api_version = 1 self.project_id = project_id def __del__(self): del self.loglevel # Loop through and tell all threads to terminate # Do not wait for them to terminate here so that all get the # message in a timely manner for a_thread in self.wake_agent_threads: self.log.debug('Telling RSE Wakeup Thread {0:} to terminate'.format(a_thread['id'])) a_thread['terminator'].set() # Now repeat and wait for them to terminate for a_thread in self.wake_agent_threads: self.log.debug('Waiting for RSE Wakeup Thread {0:} to rejoin'.format(a_thread['id'])) a_thread['thread'].join() def WakeAgents(self): """ Using the API move all agents to active poll mode Note: This may require up to 60 seconds for the agents to respond. """ if self.api_version == 1: self.ReInit(self.sslenabled, "/v1.0/user/wakeupagents") self.headers['X-Auth-Token'] = self.authenticator.AuthToken self.headers['Content-Type'] = 'application/json; charset=utf-8' self.log.debug('headers: %s', self.Headers) res = requests.post(self.Uri, headers=self.Headers) else: self.ReInit(self.sslenabled, '/v{0}/{1}/events'.format(self.api_version, self.project_id)) self.headers['X-Auth-Token'] = self.authenticator.AuthToken self.headers['Content-Type'] = 'application/json; charset=utf-8' self.headers['X-Project-Id'] = self.project_id self.o = {} self.o['event'] = 'agent_activate' self.o['mode'] = 'active' self.body = json.dumps(self.o) self.log.debug('headers: %s', self.Headers) res = requests.post(self.Uri, headers=self.Headers, data=self.Body) self.log.debug('Wake Agent: code = {0:}, reason = {1:}'.format(res.status_code, res.reason)) return res.status_code def WakeSpecificAgent(self, machine_agent_id, rse, timeoutMilliseconds, keep_agent_awake=False, wake_period=None): """ Using the API to move all agents to active poll mode and then check that a specific agent is polling. machine_agent_id - agent id for the specific agent to look for rse - instance of the cloudbackup.client.rse.Rse class to use for listening to RSE timeoutMilliseconds - maximum time to check RSE for the data keep_agent_awake - whether or not to start a thread to keep posting the wake agent wake_period - period between wake agent calls, should be less than the timeout interval for the current state of the agent normally 70 seconds should be fine. If set to None, use the Real-Time Timeout as a basis and set appropriately defaulting to 70 if too small """ # For up to timeoutMilliseconds try to wake all the agents on the account in use start_time = int(round(time.time() * 1000)) finish_time = start_time + timeoutMilliseconds wokeall = False wakeup_status_code = 0 while ((int(round(time.time() * 1000))) < finish_time): wakeup_status_code = self.WakeAgents() if self.api_version == 1: valid_code = 200 else: valid_code = 202 if wakeup_status_code == valid_code: wokeall = True break if wokeall: # For up to timeoutMilleseconds look for the specified agent's heart beat start_time = int(round(time.time() * 1000)) finish_time = start_time + timeoutMilliseconds woke_agent = False while ((int(round(time.time() * 1000))) < finish_time): if rse.MonitorForHeartBeat(machine_agent_id): woke_agent = True break if not woke_agent: # Unable to find the agent's heart beat within the timeout period self.log.error('Unable to locate agent id (' + str(machine_agent_id) + ') in RSE Heartbeats') if woke_agent: if keep_agent_awake: if wake_period is None: self.GetAgentConfiguration(machine_agent_id) agent_config = self.AgentConfiguration(machine_agent_id) rse_heartbeat_config = agent_config.RseHeartbeatConfig self.log.debug('Rse config: {0:}'.format(rse_heartbeat_config)) if self.api_version == 1: wake_period = (rse_heartbeat_config['Timeout'] ['RealTime'] / 1000) else: wake_period = (rse_heartbeat_config['timeout_ms'] ['real_time'] / 1000) # create a buffer if wake_period > 6: wake_period = wake_period - 5 elif wake_period > 2: wake_period = wake_period - 1 else: # if it's too small then default to a reasonable time frame # UX uses approximately 70 seconds wake_period = 70 self.KeepAgentAwake(machine_agent_id, rse, wake_period) return woke_agent else: # Unable to use the API to wake the agents within the timeout period self.log.error('Unable to wake all agents. Status Code = ' + str(wakeup_status_code)) return False def KeepAgentAwake(self, machine_agent_id, rse, period): """ Start a thread that will periodically post Wake Agent and check that the agent is alive Parameters: machine_agent_id - machine agent id of the agent to monitor for heart beats rse - RSE instance configured for the agent period - period between posting wake agent messages Note: period is starts after a successful find of the agent heartbeat """ wake_agent_thread = {} wake_agent_thread['id'] = machine_agent_id self.wake_agent_threads.append(wake_agent_thread) user_credentials = self.authenticator.InitialAuthCredentials for a_thread in self.wake_agent_threads: if a_thread['id'] == machine_agent_id: self.log.debug('Starting RSE Wakeup Thread for agent: {0:}'.format(machine_agent_id)) a_thread['terminator'] = threading.Event() a_thread_kwargs = { 'my_notifier': wake_agent_thread['terminator'], 'userid': user_credentials['userid'], 'credentials': user_credentials['credentials'], 'usertype': user_credentials['usertype'], 'method': user_credentials['method'], 'rse_app': rse.rsedata.app, 'rse_version': rse.rsedata.appVersion, 'rse_agentkey': rse.agentkey, 'rse_period': period, 'apihost': self.apihost, 'api_version': self.api_version, 'agent_id': machine_agent_id, 'project_id': self.project_id, 'rse_log': rse.rselogfile, 'rse_apihost': rse.apihost } a_thread['thread'] = threading.Thread(target=_keep_agent_awake_thread_fn, kwargs=a_thread_kwargs ) a_thread['thread'].start() break def StopKeepAgentWake(self, machine_agent_id): """ Stop the thread that is posting the wake agents and monitoring for the given machine agent id Parameters: machine_agent_id - the machine agent identifier that is being monitored for """ for a_thread in self.wake_agent_threads: if a_thread['id'] == machine_agent_id: self.log.debug('Telling for RSE Wakeup Thread {0:} for agent {1:} to terminate'.format(a_thread['id'], machine_agent_id)) a_thread['terminator'].set() self.log.debug('Waiting for RSE Wakeup Thread {0:} to rejoin'.format(a_thread['id'])) a_thread['thread'].join() self.wake_agent_threads.remove(a_thread) break # # Agent Details # def GetAgentDetails(self, machine_agent_id): """ Retrieve all the information regarding the specified Agent ID """ self.agents = {} if self.api_version == 1: self.ReInit(self.sslenabled, '/v1.0/agent/{0}'.format(machine_agent_id)) self.headers['X-Auth-Token'] = self.authenticator.AuthToken self.headers['Content-Type'] = 'application/json; charset=utf-8' else: self.ReInit(self.sslenabled, '/v{0}/{1}/agents/{2}'.format(self.api_version, self.project_id, machine_agent_id)) self.headers['X-Auth-Token'] = self.authenticator.AuthToken self.headers['Content-Type'] = 'application/json; charset=utf-8' self.headers['X-Project-Id'] = self.project_id res = requests.get(self.Uri, headers=self.Headers) if res.status_code == 200: self.log.debug('Agent Details(id: {0:}) - {1:}'.format(machine_agent_id, res.json())) self.agents[machine_agent_id] = AgentDetails(details=res.json(), version=self.api_version) return True else: self.log.error('Unable to retrieve agent details for agent id ' + str(machine_agent_id) + ' system return code ' + str(res.status_code) + ' reason = ' + res.reason) return False def GetAgentsFromApi(self): """ Lookup the associated agents and return a list of their IDs """ if self.api_version == 1: self.ReInit(self.sslenabled, '/v1.0/user/agents') self.headers['X-Auth-Token'] = self.authenticator.AuthToken self.headers['Content-Type'] = 'application/json; charset=utf-8' res = requests.get(self.Uri, headers=self.Headers) if res.status_code == 200: result_list = [] results = res.json() for agent in results: result_list.append(agent['MachineAgentId']) return result_list else: self.log.error('Unable to retrieve agent list system return code ' + str(res.status_code) + ' reason = ' + res.reason) return [] else: self.ReInit(self.sslenabled, '/v{0}/{1}/agents'.format( self.api_version, self.project_id )) self.headers['X-Auth-Token'] = self.authenticator.AuthToken self.headers['Content-Type'] = 'application/json; charset=utf-8' self.headers['X-Project-Id'] = self.project_id res = requests.get(self.Uri, headers=self.Headers) if res.status_code == 200: result_list = [] results = res.json() for agent in results['agents']: result_list.append(agent['id']) return result_list else: self.log.error('Unable to retrieve agent list system return code ' + str(res.status_code) + ' reason = ' + res.reason) return [] @property def GetAgentIds(self): """ Return a list of known agent ids for agents details retrieved by GetAgentDetails() """ return self.agents.keys() def AgentDetails(self, machine_agent_id): """ The AgentDetails object describing the agent with the given machine_agent_id """ try: return self.agents[machine_agent_id] except LookupError: msg = 'Machine Agent Id ({0:}) not available. Did you call GetAgentDetails() for that agent?'.format(machine_agent_id) self.log.error(msg) raise AgentDetailsNotAvailable(msg) # # Agent Logs # def GetAgentLogFile(self, machine_agent_id): """ Request a log file upload from the agent """ if self.api_version == 1: self.ReInit(self.sslenabled, '/v1.0/agent/requestlog/{0}'.format( machine_agent_id )) self.headers['X-Auth-Token'] = self.authenticator.AuthToken self.headers['Content-Type'] = 'application/json; charset=utf-8' res = requests.post(self.Uri, headers=self.Headers) if res.status_code == 200: logfile_request_id = res.json() return logfile_request_id else: self.log.error('Unable to request agent log file upload for agent id ' + str(machine_agent_id) + '. Server returned ' + str(res.status_code) + ': ' + res.text + ' Reason: ' + res.reason) return None else: self.ReInit(self.sslenabled, '/v{0}/{1}/agents/{2}/logfiles'.format( self.api_version, self.project_id, machine_agent_id )) self.headers['X-Auth-Token'] = self.authenticator.AuthToken self.headers['Content-Type'] = 'application/json; charset=utf-8' self.headers['X-Project-Id'] = self.project_id res = requests.post(self.Uri, headers=self.Headers) if res.status_code == 202: logfile_request_id = res.json()['id'] return logfile_request_id else: self.log.error('Unable to request agent log file upload for agent id ' + str(machine_agent_id) + '. Server returned ' + str(res.status_code) + ': ' + res.text + ' Reason: ' + res.reason) return None def GetExistingAgentLogFiles(self, machine_agent_id): """ List the existing agent log files """ if self.api_version == 1: self.ReInit(self.sslenabled, '/v1.0/agent/logfiles/{0}'.format( machine_agent_id )) self.headers['X-Auth-Token'] = self.authenticator.AuthToken self.headers['Content-Type'] = 'application/json; charset=utf-8' else: self.ReInit(self.sslenabled, '/v{0}/{1}/agents/{2}/logfiles'.format( self.api_version, self.project_id, machine_agent_id )) self.headers['X-Auth-Token'] = self.authenticator.AuthToken self.headers['Content-Type'] = 'application/json; charset=utf-8' self.headers['X-Project-Id'] = self.project_id res = requests.get(self.Uri, headers=self.Headers) result = [] if res.status_code == 200: if self.api_version == 1: for logfile_entry in res.json(): e = { 'id': logfile_entry['DisplayName'], 'date': logfile_entry['DisplayName'], 'status': logfile_entry['CurrentState'], 'link': logfile_entry['FilePath'] } result.append(e) else: for logfile_entry in res.json()['logfiles']: e = { 'id': logfile_entry['id'], 'date': logfile_entry['date'], 'status': logfile_entry['state'], 'link': None } # Note: rel == logfile_temp_url is only present # if the file has been uploaded for url_set in logfile_entry['links']: if url_set['rel'] == 'logfile_temp_url': e['link'] = url_set['href'] result.append(e) else: self.log.error('Unable to list uploaded agent log files for agent id ' + str(machine_agent_id) + '. Server returned ' + str(res.status_code) + ': ' + res.text + ' Reason: ' + res.reason) return result def DownloadAgentLogFile(self, logfile_data, target_filename): try: try: headers = { 'X-Auth-Token': self.authenticator.AuthToken } res = requests.get( logfile_data['link'], stream=True, headers=self.headers ) except requests.exceptions.SSLError as ex: self.log.error('Requests SSLError: {0}'.format(str(ex))) res = requests.get(logfile_data['link'], verify=False, stream=True) if res.status_code == 404: raise UserWarning('Temp URL invalid') elif res.status_code >= 300: raise UserWarning('Server responded unexpectedly during download (Code: ' + str(res.status_code) + ' )') file_chunk_size = 4 * 1024 * 1024 etag_match = None if 'Etag' in res.headers: etag_match = res.headers['Etag'] meter = {} meter['bytes-total'] = int(res.headers['Content-Length']) meter['bytes-remaining'] = int(res.headers['Content-Length']) meter['bar-count'] = 50 meter['bytes-per-bar'] = meter['bytes-remaining'] // meter['bar-count'] meter['block-size'] = min(file_chunk_size, meter['bytes-per-bar']) meter['chunks-per-bar'] = meter['bytes-per-bar'] // meter['block-size'] meter['chunks'] = 0 meter['bars-remaining'] = meter['bar-count'] meter['bars-completed'] = 0 self.log.info('Downloading logfile(gz): {0} bytes...'.format(meter['bytes-remaining'])) self.log.info('[' + ' ' * meter['bar-count'] + ']') gzip_file = target_filename + '.gz' compressed_md5_hash = hashlib.md5() with open(gzip_file, 'wb') as gzipped_db: for lf_chunk in res.iter_content(chunk_size=meter['block-size']): gzipped_db.write(lf_chunk) compressed_md5_hash.update(lf_chunk) gzipped_db.flush() os.fsync(gzipped_db.fileno()) meter['chunks'] += 1 if meter['chunks'] == meter['chunks-per-bar']: meter['chunks'] = 0 meter['bars-completed'] += 1 meter['bars-remaining'] -= 1 self.log.info('[' + '-' * meter['bars-completed'] + ' ' * meter['bars-remaining'] + ']') if etag_match is not None: if etag_match.upper() != compressed_md5_hash.hexdigest().upper(): raise UserWarning( 'Failed to download. {0} != {1}'.format( etag_match.upper(), compressed_md5_hash.hexdigest().upper() ) ) self.log.info('Decompressing the file...') gz_lf_file = gzip.open(gzip_file, 'rb') with open(target_filename, 'wb') as lf_file: decompress_continue_loop = True while decompress_continue_loop: filechunk = gz_lf_file.read(file_chunk_size) if len(filechunk) == 0: decompress_continue_loop = False else: lf_file.write(filechunk) gz_lf_file.close() return True except Exception as ex: self.log.error('Failed to download file: {0}'.format(ex)) return False # # Agent Configurations # def GetAgentConfiguration(self, machine_agent_id): """ Retrieve the Configuration for the given agent """ if self.api_version == 1: self.ReInit(self.sslenabled, '/v1.0/agent/configuration/{0}'.format( machine_agent_id ) ) self.headers['X-Auth-Token'] = self.authenticator.AuthToken self.headers['Content-Type'] = 'application/json; charset=utf-8' else: self.ReInit(self.sslenabled, '/v{0}/{1}/agents/{2}/configuration'.format( self.api_version, self.project_id, machine_agent_id ) ) self.headers['X-Auth-Token'] = self.authenticator.AuthToken self.headers['Content-Type'] = 'application/json; charset=utf-8' self.headers['X-Project-Id'] = self.project_id res = requests.get(self.Uri, headers=self.Headers) if res.status_code == 200: self.configurations[machine_agent_id] = AgentConfiguration( configuration=res.json(), version=self.api_version) return True else: self.log.error('Unable to retrieve agent configuration for agent id ' + str(machine_agent_id) + '. Server returned ' + str(res.status_code) + ': ' + res.text + ' Reason: ' + res.reason) return False @property def AgentConfigurationIds(self): """ Return a list of known agent ids for agent configurations retrieved by GetAgentConfiguration() """ return self.configurations.keys() def AgentConfiguration(self, machine_agent_id): """ Return the AgentConfiguration object containing the configuration for the agent with the given machine_agent_id """ try: return self.configurations[machine_agent_id] except LookupError: msg = 'Machine Agent Id ({0:}) not available. Did you call GetAgentConfiguration() for that agent?'.format(machine_agent_id) self.log.error(msg) raise AgentConfigurationNotAvailable(msg) # # Agent Activity # def GetAgentLatestActivity(self, machine_agent_id): """ Retrieve the current activities of the agent """ # Get the agent configuration so that we know we can lookup the backup configs in order # to display a useful name about the activity to the user self.GetAgentConfiguration(machine_agent_id) agent_config = self.AgentConfiguration(machine_agent_id) if self.api_version == 1: self.ReInit(self.sslenabled, '/v1.0/{0}/system/activity{1}'.format( self.authenticator.AuthTenantId, machine_agent_id ) ) self.headers['X-Auth-Token'] = self.authenticator.AuthToken self.headers['Content-Type'] = 'application/json; charset=utf-8' else: self.ReInit(self.sslenabled, '/v{0}/{1}/agents/{2}/activities'.format( self.api_version, self.project_id, machine_agent_id ) ) self.headers['X-Auth-Token'] = self.authenticator.AuthToken self.headers['Content-Type'] = 'application/json; charset=utf-8' self.headers['X-Project-Id'] = self.project_id res = requests.get(self.Uri, headers=self.Headers) if res.status_code == 200: results = [] if self.api_version == 1: for activity in res.json(): activity_name = '' if activity['ParentId'] != 0: try: activity_name = '{0} - {1}'.format( activity['Type'], agent_config.GetBackupNameFromId( activity['ParentId'] ) ) except: activity_name = '{0} - UNKNOWN({1})'.format( activity['Type'], activity['ParentId'] ) else: activity_name = '{0} - {1}'.format( activity['Type'], activity['DisplayName'] ) results.append( { 'id': activity['Id'], 'name': activity_name, 'type': activity['Type'], 'state': activity['CurrentState'], 'time': activity['TimeOfActivity'] } ) else: activities = res.json()['activities'] activities.reverse() for activity in activities: activity_name = '' if 'configuration' in activity.keys(): try: activity_name = '{0} - {1}'.format( activity['type'], agent_config.GetBackupNameFromId( activity['configuration']['id'] ) ) except: activity_name = '{0} - UNKNOWN({1})'.format( activity['type'], activity['configuration']['id'] ) else: activity_name = activity['type'] results.append( { 'id': activity['id'], 'name': activity_name, 'type': activity['type'], 'state': activity['state'], 'time': activity['last_updated_time'] } ) return results else: self.log.error('Unable to retrieve latest agent activities for agent id ' + str(machine_agent_id) + '. Server returned ' + str(res.status_code) + ': ' + res.text + ' Reason: ' + res.reason) return [] def GetAgentEventsSince(self, machine_agent_id, last_event_id, event_limit=100, results={}): """ Retrieve the events of the agent since the last events id specified { 'heartbeats': [ <event>,.. ], 'backup <id>: [ <event>,.. ], 'restore <id>: [ <event>,.. ],.. """ # Get the agent configuration so that we know we can lookup the backup configs in order # to display a useful name about the activity to the user self.GetAgentConfiguration(machine_agent_id) agent_config = self.AgentConfiguration(machine_agent_id) flip_activity_list_due_to_api_inconsistency = False if self.api_version == 1: self.ReInit(self.sslenabled, '/v1.0/{0}/system/activity{1}'.format( self.authenticator.AuthTenantId, machine_agent_id ) ) self.headers['X-Auth-Token'] = self.authenticator.AuthToken self.headers['Content-Type'] = 'application/json; charset=utf-8' else: if last_event_id is None: self.ReInit(self.sslenabled, '/v{0}/{1}/agents/{2}/events'.format( self.api_version, self.project_id, machine_agent_id ) ) else: self.ReInit(self.sslenabled, '/v{0}/{1}/agents/{2}/events?marker={3}&limit={4}&sort_dir=asc'.format( self.api_version, self.project_id, machine_agent_id, last_event_id, event_limit ) ) flip_activity_list_due_to_api_inconsistency = True self.headers['X-Auth-Token'] = self.authenticator.AuthToken self.headers['Content-Type'] = 'application/json; charset=utf-8' self.headers['X-Project-Id'] = self.project_id res = requests.get(self.Uri, headers=self.Headers) if res.status_code == 200: new_last_event_id = None if self.api_version == 1: # TODO: v1 doesn't have Events like V2 does... # v1 API we look at the Id as it is monotonously increasing if last_event_id is None: new_last_event_id = 0 else: new_last_event_id = last_event_id for activity in res.json(): activity_name = '' if activity['ParentId'] != 0: try: activity_name = '{0} - {1}'.format( activity['Type'], agent_config.GetBackupNameFromId( activity['ParentId'] ) ) except: activity_name = '{0} - UNKNOWN({1})'.format( activity['Type'], activity['ParentId'] ) else: activity_name = '{0} - {1}'.format( activity['Type'], activity['DisplayName'] ) if activity['Id'] > last_event_id: results.append( { 'id': activity['Id'], 'name': activity_name, 'type': activity['Type'], 'state': activity['CurrentState'], 'time': activity['TimeOfActivity'] } ) new_last_event_id = activity['Id'] # Reverse the order so that the newest is at the start results.reverse() else: events = res.json()['events'] if flip_activity_list_due_to_api_inconsistency: events.reverse() new_last_event_id = last_event_id if new_last_event_id is None: new_last_event_id = 0 if not 'heartbeats' in results.keys(): results['heartbeats'] = [] for event in events: # update the last event if event['id'] > new_last_event_id: new_last_event_id = event['id'] # filter heart beats if 'event' in event.keys(): if 'heartbeat' in event['event']: results['heartbeats'].append(event) else: event_name = None # filter backups if 'backup' in event.keys(): if 'id' in event['backup'].keys(): event_name = 'Backup {0}'.format( event['backup']['id'] ) # filter restores elif 'restore' in event.keys(): if 'id' in event['restore'].keys(): event_name = 'Restore {0}'.format( event['restore']['id'] ) if not event_name is None: if not event_name in results.keys(): results[event_name] = [] results[event_name].append(event) else: # Dump everything else based on the size of the results # so that they enter in order results[len(results)] = event return (results, new_last_event_id) else: self.log.error('Unable to retrieve latest agent events for agent id ' + str(machine_agent_id) + '. Server returned ' + str(res.status_code) + ': ' + res.text + ' Reason: ' + res.reason) return ([], last_event_id) # # Agent Cleanup # def GetAllAgentsForHost(self, cloud_server_name=None, cloud_server_id=None, cloud_server_ips=None): """ Retrieve a list (set) of agent identifiers for a given cloud server cloud_server_name - the name of the cloud server from Rackspace ControlPanel, also available via the bootstrap details and GetAgentDetails() Returns a set of dictionaries containing the following data: AgentVersion Architecture Flavor BackupVaultSize CleanupAllowed Datacenter IPAddress IsDisabled IsEncrypted MachineAgentId MachineName OperatingSystem OperatingSystemVersion PublicKey Status TimeOfLastSuccessfulBackup UseServiceNet HostServerId """ if cloud_server_name is None and cloud_server_id is None and cloud_server_ips is None: raise ParameterError('Neither Cloud Server Name nor Cloud Server Id (HostServerId) nor Cloud Server IPs were specified. Unable to match a server.') if self.api_version == 1: self.ReInit(self.sslenabled, "/v1.0/user/agents") self.headers['X-Auth-Token'] = self.authenticator.AuthToken self.headers['Content-Type'] = 'application/json; charset=utf-8' res = requests.get(self.Uri, headers=self.Headers) if res.status_code == 200: agentlist = list() try: usersagentlist = res.json() for agent in usersagentlist: self.log.debug('Agent: ' + str(agent)) if (cloud_server_id is not None and 'HostServerId' in agent): self.log.debug( 'Checking Id Match: {0:} == {1:}' .format(cloud_server_id, agent['HostServerId'])) if agent['HostServerId'] == cloud_server_id: self.log.debug('Id Matched: Adding ' + str(agent)) agentlist.append(agent) continue if (cloud_server_name is not None and 'MachineName' in agent): self.log.debug( 'Checking Name Match: {0:} == {1:}' .format(cloud_server_name, agent['MachineName'])) if agent['MachineName'] == cloud_server_name: self.log.debug('Name Matched: Adding ' + str(agent)) agentlist.append(agent) continue if (cloud_server_ips is not None and 'IPAddress' in agent): self.log.debug( 'Checking IP Match: {0:} in {1:}' .format(agent['IPAddress'], cloud_server_ips)) if agent['IPAddress'] in cloud_server_ips: self.log.debug('IP Matched: Adding ' + str(agent)) agentlist.append(agent) continue except LookupError: self.log.error('Unable to retrieve all agents from the ' 'returned agent list') self.log.error('system response: ' + res.text) self.log.error('system reason: ' + res.reason) return agentlist else: if cloud_server_name is not None: self.log.error('Unable to retrieve all agents for cloud ' 'server (name: ' + cloud_server_name + ') system return code ' + str(res.status_code)) if cloud_server_id is not None: self.log.error('Unable to retrieve all agents for cloud ' 'server (id: ' + cloud_server_id + ') system return code ' + str(res.status_code)) self.log.error('system response: ' + res.text) self.log.error('system reason: ' + res.reason) return list() else: self.ReInit(self.sslenabled, '/v{0}/{1}/agents'.format(self.api_version, self.project_id)) self.headers['X-Auth-Token'] = self.authenticator.AuthToken self.headers['Content-Type'] = 'application/json; charset=utf-8' res = requests.get(self.Uri, headers=self.Headers) if res.status_code == 200: resp_body = res.json() agentlist = list() for entry in resp_body['agents']: if cloud_server_id: if entry['host']['machine']['id'] == cloud_server_id: agentlist.append(entry) continue if cloud_server_name: if entry['name'] == cloud_server_name: agentlist.append(entry) continue if cloud_server_ips: for address in entry['host']['addresses']: if address['addr'] in cloud_server_ips: agentlist.append(entry) continue return agentlist else: if cloud_server_name is not None: self.log.error('Unable to retrieve all agents for cloud ' 'server (name: ' + cloud_server_name + ') system return code ' + str(res.status_code)) if cloud_server_id is not None: self.log.error('Unable to retrieve all agents for cloud ' 'server (id: ' + cloud_server_id + ') system return code ' + str(res.status_code)) self.log.error('system response: ' + res.text) self.log.error('system reason: ' + res.reason) return list() def RemoveAgent(self, machine_agent_id): """ De-register the agent from the Rackspace Cloud Backup API """ if self.api_version == 1: self.ReInit(self.sslenabled, '/v1.0/agent/delete') self.headers['X-Auth-Token'] = self.authenticator.AuthToken self.headers['Content-Type'] = 'application/json; charset=utf-8' self.o = {} self.o['MachineAgentId'] = machine_agent_id self.body = json.dumps(self.o) res = requests.post(self.Uri, headers=self.Headers, data=self.Body) if res.status_code == 204: self.log.info('Removed agent id ' + str(machine_agent_id)) self.log.warn('Please restart the process to lookup this agent again as the agent id may have changed.') return True else: self.log.error('Unable to remove agent id ' + str(machine_agent_id) + ' system return code ' + str(res.status_code) + ' Reason: ' + res.reason) return False else: self.ReInit(self.sslenabled, '/v{0}/{1}/agents/{2}'.format(self.api_version, self.project_id, machine_agent_id)) self.headers['X-Auth-Token'] = self.authenticator.AuthToken self.headers['Content-Type'] = 'application/json; charset=utf-8' res = requests.delete(self.Uri, headers=self.Headers) if res.status_code == 204: self.log.info('Removed agent id ' + str(machine_agent_id)) self.log.warn('Please restart the process to lookup this ' 'agent again as the agent id may have changed.') return True else: self.log.error('Unable to remove agent id ' + str(machine_agent_id) + ' system return code ' + str(res.status_code) + ' Reason: ' + res.reason) return False def RemoveAllAgentsForHost(self, agent_list): """ Remove all agents in the system registered to the same user using the same host server id host_server_id - the host server id to remove agents from, """ agents_removed = [] for agent in agent_list: if self.RemoveAgent(agent['MachineAgentId']): agents_removed.append(agent['MachineAgentId']) return agents_removed def EnableDisableAgent(self, machine_agent_id, enabled=True): """ Enable or Disable an agent """ # TODO: update for v2 API self.ReInit(self.sslenabled, "/v1.0/agent/enable") self.headers['X-Auth-Token'] = self.authenticator.AuthToken self.headers['Content-Type'] = 'application/json; charset=utf-8' self.o = {} self.o['MachineAgentId'] = machine_agent_id self.o['Enable'] = enabled self.body = json.dumps(self.o) res = requests.post(self.Uri, headers=self.Headers, data=self.Body) if res.status_code == 204: # success self.log.info('Changed Agent Status - Machine Agent Id: {0:}, Enabled: {1:}'.format(machine_agent_id, enabled)) return True elif res.status_code == 401: # bad credentials self.log.warn('Invalid AuthToken') return False elif res.status_code == 403: # no permissions self.log.warn('User does not have permission to enable/disable this system.') return False else: # other issue - 400, 500, 503, or something else self.log.error('Error (code: {0:}): {1:}'.format(res.status_code, res.text)) return False

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81,054

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0

null

0

null

0

1

0

fac87337edea7980e0ccd0e7caedefd311d386c3

987

py

Python

tests/test_graphql.py

timfeirg/flask-graphene-boilerplate

fea654f76bd38ed46effe36fb644d4b2ce27bd0f

[ "MIT" ]

8

2018-04-06T12:50:10.000Z

2021-07-09T11:50:28.000Z

tests/test_graphql.py

timfeirg/flask-graphene-boilerplate

fea654f76bd38ed46effe36fb644d4b2ce27bd0f

[ "MIT" ]

null

tests/test_graphql.py

timfeirg/flask-graphene-boilerplate

fea654f76bd38ed46effe36fb644d4b2ce27bd0f

[ "MIT" ]

2

2021-01-29T14:43:24.000Z

2021-06-24T07:54:27.000Z

import json def dumpdump(s): """double json.dumps string""" return json.dumps(json.dumps(s)) def test_crud(graphene_client): # test create object sample_key = 'whatever' sample_value = {'foo': 'bar'} query = ''' mutation testCreateItem { createItem(key: %s, value: %s) { ok item { key value } } } ''' % (json.dumps(sample_key), dumpdump(sample_value)) res = graphene_client.execute(query) assert res['data']['createItem']['ok'] is True created_item = res['data']['createItem']['item'] assert created_item['key'] == sample_key assert json.loads(created_item['value']) == sample_value # test delete object query = ''' mutation testDeleteItem { deleteItem(key: %s) { ok } } ''' % (json.dumps(sample_key)) res = graphene_client.execute(query) assert res['data']['deleteItem']['ok'] is True

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987

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0.064516

false

0

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0

0.129032

0

null

0

null

0

1

0

fac919db5169d18f0fdc7a8a783370177da14510

2,226

py

Python

src/resize.py

MikeLrUC/IA-Project

12d1ec80a8253b5493ea928809e3357dfe5d596e

[ "MIT" ]

null

src/resize.py

MikeLrUC/IA-Project

12d1ec80a8253b5493ea928809e3357dfe5d596e

[ "MIT" ]

null

src/resize.py

MikeLrUC/IA-Project

12d1ec80a8253b5493ea928809e3357dfe5d596e

[ "MIT" ]

1

2022-02-19T23:43:19.000Z

import os import cv2 # OpenCV lib for image manipulation RAW_ROOT = "../data/PVTL_dataset/" PCD_ROOT = "../data/processed/" TRAIN_PATH = "train/" VAL_PATH = "val/" def get_image_names(path_type): return [f"{path_type}{img_class}/{image}" for img_class in os.listdir(path_type) for image in os.listdir(f"{path_type}{img_class}")] def get_sizes(images): widths, heights = [], [] for image in images: img = cv2.imread(image, cv2.IMREAD_UNCHANGED) widths.append(img.shape[1]) heights.append(img.shape[0]) return widths, heights def resize_and_pad_images(images, w_target, h_target): for image in images: # Image img = cv2.imread(image, cv2.IMREAD_UNCHANGED) # Getting minimum ratio (to maintain image format) ratio_w = w_target / img.shape[1] ratio_h = h_target / img.shape[0] desired_ratio = min(ratio_w, ratio_h) # Resizing desired_size = [int(desired_ratio * img.shape[0]), int(desired_ratio * img.shape[1])] resized = cv2.resize(img, (desired_size[1], desired_size[0])) # Padding and Centering Image delta_w = w_target - desired_size[1] delta_h = h_target - desired_size[0] top, bottom = delta_h // 2, delta_h - (delta_h // 2) left, right = delta_w // 2, delta_w - (delta_w // 2) padded = cv2.copyMakeBorder(resized, top, bottom, left, right, cv2.BORDER_CONSTANT, value=[0,0,0]) # Writing Image to Processed Path cv2.imwrite(PCD_ROOT + image[len(RAW_ROOT):], padded) if __name__ == "__main__": # Getting Training and Validation Images train_images = get_image_names(RAW_ROOT + TRAIN_PATH) validation_images = get_image_names(RAW_ROOT + VAL_PATH) total_images = train_images + validation_images # Getting Maximum sizes widths, heights = get_sizes(total_images) w_max, h_max = max(widths), max(heights) w_max, h_max = 224, 224 # Hardcoded # Resizing and Padding Images to maximum Width and Height, maintaining picture ratio resize_and_pad_images(total_images, w_max, h_max) print(f"Resized and Padded Images to w,h = ({w_max}, {h_max})") # (388, 884)

37.1

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320

2,226

4.3125

0.278125

0.034783

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0.023188

0.173913

0.115942

0.050725

0

0.022157

0.22956

2,226

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37.728814

0.782507

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0

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0

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0.038584

0

1

0.078947

false

0

0.052632

0.026316

0.184211

0.026316

0

null

0

null

0

1

0

facbc305d420d17489ac86c0fe411bc028552bc2

2,622

py

Python

roughtime/timeserver/gps_time_daemon/ntptimesource.py

matzf/scion-apps

5927b00b3f97697b268a34cada5d683d36ed5dae

[ "Apache-2.0" ]

3

2018-02-22T16:50:42.000Z

2018-06-15T12:50:23.000Z

roughtime/timeserver/gps_time_daemon/ntptimesource.py

matzf/scion-apps

5927b00b3f97697b268a34cada5d683d36ed5dae

[ "Apache-2.0" ]

21

2017-11-24T16:22:19.000Z

2018-08-30T06:27:22.000Z

roughtime/timeserver/gps_time_daemon/ntptimesource.py

matzf/scion-apps

5927b00b3f97697b268a34cada5d683d36ed5dae

[ "Apache-2.0" ]

12

2017-11-23T08:20:10.000Z

2018-07-26T14:37:58.000Z

import threading import ntplib from time import ctime from datetime import datetime from dateutil import tz from dateutil.tz import tzlocal def query_ntp_server(server_url, request_timeout, result_handler): client = ntplib.NTPClient() try: response = client.request(server_url, version=3, timeout=request_timeout) result_handler(response) except: print("Error getting time from ntp server: %s" % (server_url)) class TimeResult: def __init__(self): self.responses=[] self.response_lock=threading.Lock() def response_received(self, response): self.response_lock.acquire() self.responses.append(response) self.response_lock.release() def get_time(self): times=[] for r in self.responses: times.append(r.tx_time) return self._find_max_window_time(times) def _find_max_window_time(self, obtained_times): """ We take time that has largest number of occurrences, within delta """ if len(obtained_times)==0: return 0, 0 obtained_times.sort() start=0 end=0 max_window=0 t=0 while end<len(obtained_times): delta=obtained_times[end]-obtained_times[start] if delta <= NTPTimeSource.MAX_DELTA_SEC: end=end+1 window_size=end-start if window_size>max_window: max_window=window_size t=obtained_times[start] else: start=start+1 return t, max_window class NTPTimeSource: MAX_DELTA_SEC=2 def __init__(self, ntp_servers, request_timeout): self.servers=ntp_servers self.timeout=request_timeout def get_ntp_time(self): """ Query all ntp servers in different threads, take the time that has most occurrences """ response=TimeResult() workers=[] for server in self.servers: t=threading.Thread(target=query_ntp_server, args=(server, self.timeout, response.response_received, )) t.start() workers.append(t) for worker in workers: worker.join() timestamp, server_num = response.get_time() return datetime.fromtimestamp(timestamp).replace(tzinfo=tzlocal()), server_num if __name__ == "__main__": print("Sending query to NTP servers") ntp_servers=["0.pool.ntp.org", "3.ch.pool.ntp.org", "3.europe.pool.ntp.org", "europe.pool.ntp.org"] ntp_source=NTPTimeSource(ntp_servers, 5) t, server_num = ntp_source.get_ntp_time() print(t)

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0.044776

0

null

0

null

0

1

0

facd051b66ae50bb7afdfaf52a9dd8b00737ecd8

3,707

py

Python

binary_tree/functions.py

RakitinDen/pytorch-recursive-gumbel-max-trick

44f9854020e727946a074a6e53b20dd593f96cc1

[ "Apache-2.0" ]

20

2021-12-03T13:20:17.000Z

2022-03-20T18:58:06.000Z

binary_tree/functions.py

RakitinDen/pytorch-recursive-gumbel-max-trick

44f9854020e727946a074a6e53b20dd593f96cc1

[ "Apache-2.0" ]

null

binary_tree/functions.py

RakitinDen/pytorch-recursive-gumbel-max-trick

44f9854020e727946a074a6e53b20dd593f96cc1

[ "Apache-2.0" ]

null

import torch import sys sys.path.append('../') from binary_tree.utils import BinaryTree, build_tree from estimators import uniform_to_exp def bin_tree_struct(exp, lengths=None, **kwargs): ''' Defines F_struct for binary tree Applies the divide and conquer algorithm from the paper Input -------------------- exp : torch.Tensor | batch_size x dim | Contains a batch of arrays lengths : torch.Tensor | batch_size | Contains lengths of arrays in the batch (lengths[i] <= dim) **kwargs : Needed to support usage of different F_struct in the estimators' implementation Output -------------------- struct_var : BinaryTree (defined in binary_tree.utils) ''' batch_size = exp.shape[0] dim = exp.shape[1] masks = -torch.log(torch.eye(dim).unsqueeze(0).repeat(batch_size, 1, 1)) trees = [] heights = torch.zeros(batch_size) for batch_idx in range(batch_size): if lengths is None: right = dim else: right = lengths[batch_idx].item() left = 0 level = 0 tree = build_tree(batch_idx, exp, left, right, level, masks, heights) trees.append(tree) struct_var = BinaryTree(masks, trees, heights) return struct_var def bin_tree_log_prob(struct_var, logits, **kwargs): ''' Defines F_log_prob for binary tree Calculates the log probability log(p(X)) of the binary tree Note: here the execution trace is in one-to-one correspondance with the binary tree itself Input -------------------- struct_var : BinaryTree (defined in binary_tree.utils) logits : torch.Tensor | batch_size x dim | Contains parameters (log(mean)) of the exponential distributions of elements in arrays **kwargs : Needed to support usage of different F_log_prob in the estimators' implementation Output -------------------- log_prob : torch.Tensor | batch_size | Contains log probabilities of the binary trees ''' batch_size = logits.shape[0] dim = logits.shape[1] logits_expanded = logits.unsqueeze(1).repeat((1, dim, 1)) masked_logits = struct_var.masks + logits_expanded log_probs = -logits - torch.logsumexp(-masked_logits, dim=-1) return log_probs.sum(dim=-1) def bin_tree_cond(struct_var, logits, uniform, **kwargs): ''' Defines F_cond for arborescence Samples from the conditional distribution p(E | T) of exponentials given the execution trace Input -------------------- struct_var : BinaryTree (defined in binary_tree.utils) logits : torch.Tensor | batch_size x dim | Contains parameters (log(mean)) of the exponential distributions of elements in arrays uniform : torch.Tensor | batch_size x dim | Contains realizations of the independent uniform variables, that will be transformed to conditional samples **kwargs : Needed to support usage of different F_cond in the estimators' implementation Output -------------------- cond_exp : torch.Tensor | batch_size x dim | Contains conditional samples from p(E | X) = p(E | T) ''' batch_size = logits.shape[0] dim = logits.shape[1] logits_expanded = logits.unsqueeze(1).repeat((1, dim, 1)) masked_logits = struct_var.masks + logits_expanded min_logits = -torch.logsumexp(-masked_logits, dim=-1) minimums = uniform_to_exp(logits=min_logits, uniform=uniform) bin_mask = torch.exp(-struct_var.masks) cond_exp = (minimums.unsqueeze(-1).repeat((1, 1, dim)) * bin_mask).sum(dim=1) return cond_exp

33.098214

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0.641489

482

3,707

4.790456

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0.060632

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0.008587

0.246021

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0

0.25641

0

null

0

null

0

1

0

facd77efc7419405e7d0573742a3ad604a3fd07b

569

py

Python

python365/dash_app.py

JonasPC/WebApp_dummy

d12fc804eda42dec495d828c85fe504861fd903b

[ "MIT" ]

null

python365/dash_app.py

JonasPC/WebApp_dummy

d12fc804eda42dec495d828c85fe504861fd903b

[ "MIT" ]

2

2021-03-31T18:55:10.000Z

2021-12-13T19:49:21.000Z

python365/dash_app.py

JonasPC/WebApp_dummy

d12fc804eda42dec495d828c85fe504861fd903b

[ "MIT" ]

null

import dash import dash_core_components as dcc import dash_html_components as html from flask import Flask server = Flask(__name__) app = dash.Dash(server=server) app.css.append_css({ 'external_url': ( 'https://cdn.rawgit.com/chriddyp/0247653a7c52feb4c48437e1c1837f75' '/raw/a68333b876edaf62df2efa7bac0e9b3613258851/dash.css' ) }) app.layout = html.Div([ html.H1(children='Hello Dash'), html.Div(children='Dash: A web application framework for Python.') ]) if __name__ == '__main__': app.run_server(debug=True, host='0.0.0.0')

22.76

74

0.718805

74

569

5.27027

0.567568

0.076923

0.015385

0

0.108108

0.154657

569

24

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0.094903

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0

0.222222

0

null

0

null

0

1

0

faceab5bf72239cbfa42db14f25288736d2a6601

1,022

py

Python

examples/drawing/draw_many.py

xxao/pero

a7f0c84fae0b21fe120204e798bd61cdab3a125d

[ "MIT" ]

13

2019-07-15T17:51:21.000Z

2022-03-15T06:13:43.000Z

examples/drawing/draw_many.py

xxao/pero

a7f0c84fae0b21fe120204e798bd61cdab3a125d

[ "MIT" ]

1

2021-12-29T00:46:44.000Z

2022-01-21T16:18:48.000Z

examples/drawing/draw_many.py

xxao/pero

a7f0c84fae0b21fe120204e798bd61cdab3a125d

[ "MIT" ]

3

2020-09-27T14:31:45.000Z

2022-01-22T14:28:15.000Z

# Created byMartin.cz # Copyright (c) Martin Strohalm. All rights reserved. import pero import numpy # init size width = 400 height = 300 padding = 50 # init data x_data = numpy.linspace(-numpy.pi, numpy.pi, 50) y_data = numpy.sin(x_data) # init scales x_scale = pero.LinScale( in_range = (min(x_data), max(x_data)), out_range = (padding, width-padding)) y_scale = pero.LinScale( in_range = (-1, 1), out_range = (height-padding, padding)) color_scale = pero.GradientScale( in_range = (-1, 1), out_range = pero.colors.Spectral) # init marker marker = pero.Circle( size = 8, x = lambda d: x_scale.scale(d[0]), y = lambda d: y_scale.scale(d[1]), line_color = lambda d: color_scale.scale(d[1]).darker(.2), fill_color = lambda d: color_scale.scale(d[1])) # init image image = pero.Image(width=width, height=height) # fill image.fill("w") # draw points marker.draw_many(image, zip(x_data, y_data)) # show image image.show()

21.291667

63

0.641879

155

1,022

4.083871

0.367742

0.039494

0.06951

0.056872

0.210111

0.14534

0.091627

0

0.025126

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1,022

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0.770101

0.151663

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false

0

0.076923

0

0.076923

0

null

0

null

0

1

0

fad681be96329f06b12884a3b3c5e3a09957a82d

703

py

Python

SuperGlue/AttentionalPropagation.py

fun-math/SLAM_with_ML

be5cc996baac3d67e2c65e60fadc6bada3f80b42

[ "MIT" ]

1

2021-08-19T06:55:53.000Z

SuperGlue/AttentionalPropagation.py

fun-math/SLAM_with_ML

be5cc996baac3d67e2c65e60fadc6bada3f80b42

[ "MIT" ]

null

SuperGlue/AttentionalPropagation.py

fun-math/SLAM_with_ML

be5cc996baac3d67e2c65e60fadc6bada3f80b42

[ "MIT" ]

null

import tensorflow as tf from MultiHeadAttention import * from MLP import * class AttentionalPropagation(tf.keras.layers.Layer): def __init__(self, feature_dim, num_heads): super(AttentionalPropagation,self).__init__() self.attention = MultiHeadAttention(num_heads, feature_dim) self.mlp = MLP([feature_dim*2, feature_dim*2, feature_dim]) # tf.zeros_like(self.mlp[-1].bias) Set bias to zero def call(self, x, source): msg = self.attention(x, source, source) return self.mlp(tf.concat([x,msg], axis=1)) if __name__=='__main__': layer=AttentionalPropagation(256,4) x=tf.random.normal(shape=(2,256,4)) y=tf.random.normal(shape=(2,256,5)) print(layer(x,y).shape)

30.565217

63

0.719772

104

703

4.634615

0.451923

0.103734

0.045643

0.074689

0.161826

0.095436

0

0.029703

0.13798

703

22

64

31.954545

0.765677

0.069701

0

0.012289

0

1

0.125

false

0

0.1875

0

0.4375

0.0625

0

null

0

null

0

1

0

fad77b7c1138a226935346eeaa3c8ba842dc09b3

1,683

py

Python

dynamodb_doctor/relationships/test_many.py

c-py/dynamodb_doctor

966a5844b92206b2a66e139cdbe4a274579be650

[ "MIT" ]

null

dynamodb_doctor/relationships/test_many.py

c-py/dynamodb_doctor

966a5844b92206b2a66e139cdbe4a274579be650

[ "MIT" ]

null

dynamodb_doctor/relationships/test_many.py

c-py/dynamodb_doctor

966a5844b92206b2a66e139cdbe4a274579be650

[ "MIT" ]

null

import pytest import aioboto3 from dynamodb_doctor import Model, String, Many from dynamodb_doctor.exceptions import MissingAttributeException ENDPOINT_URL = "http://localhost:58000" @pytest.mark.asyncio async def test_model_with_many_to_model_relationship(table_fixture): class TestModelA(Model): name = String() class Meta: table = table_fixture class TestModelB(Model): relation = Many(TestModelA) class Meta: table = table_fixture test_model = TestModelB() await test_model.save() session = aioboto3.Session() async with session.resource('dynamodb', endpoint_url=ENDPOINT_URL) as resource: table = await resource.Table(table_fixture._name) item = await table.get_item(Key={"pk": test_model._pk, "sk": test_model._sk}) assert("Item" in item) @pytest.mark.asyncio async def test_can_add_to_model_with_many_relationship(table_fixture): class TestModelA(Model): name = String() class Meta: table = table_fixture class TestModelB(Model): relation = Many(TestModelA) class Meta: table = table_fixture test_model = TestModelB() test_model.relation.add(name="number1") test_model.relation.add({"name": "number2"}) await test_model.save() test_models = await TestModelB.all() assert(len(test_models) == 1) assert(len(test_models[0].relation) == 2) assert test_models[0].relation[0].name == "number1" assert test_models[0].relation[1].name == "number2" @pytest.mark.asyncio async def test_model_with_many_to_attribute_relationship_fails(table_fixture): ...

25.5

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0.271845

0.081227

0.076715

0.068592

0.495487

0.40704

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0

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1,683

66

86

25.5

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1

0

false

0

0.090909

0

0.363636

0

null

0

null

0

1

0

fad888a3651e1b2e3993ef5bd617082eb49b208a

1,179

py

Python

Back-End/Python/MultiProcessing-Threading/MultiProcessing/process_name_2.py

ASHISHKUMAR2411/Programming-CookBook

9c60655d64d21985ccb4196360858d98344701f9

[ "MIT" ]

25

2021-04-28T02:51:26.000Z

2022-03-24T13:58:04.000Z

Back-End/Python/MultiProcessing-Threading/MultiProcessing/process_name_2.py

ASHISHKUMAR2411/Programming-CookBook

9c60655d64d21985ccb4196360858d98344701f9

[ "MIT" ]

1

2022-03-03T23:33:41.000Z

2022-03-03T23:35:41.000Z

Back-End/Python/MultiProcessing-Threading/MultiProcessing/process_name_2.py

ASHISHKUMAR2411/Programming-CookBook

9c60655d64d21985ccb4196360858d98344701f9

[ "MIT" ]

15

2021-05-30T01:35:20.000Z

2022-03-25T12:38:25.000Z

from multiprocessing import Process, current_process import time import os def worker(): name = current_process().name print('==='*15 + ' < ' + f'{name}' + ' > ' + '==='*15) time.sleep(1) print(f'{name} Exiting...') def worker_1(): name = current_process().name print('===' * 15 + ' < ' + f'{name}' + ' > ' + '===' * 15) time.sleep(1) print(f'{name} Exiting...') def service_a(): name = current_process().name print('===' * 15 + ' < ' + f'{name}' + ' > ' + '===' * 15) time.sleep(1) print(f'{name} Exiting...') def service_b(): name = current_process().name print('===' * 15 + ' < ' + f'{name}' + ' > ' + '===' * 15) time.sleep(1) print(f'{name} Exiting...') if __name__ == '__main__': serviceA = Process(name='Service A', target=service_a) serviceB = Process(name='Service B', target=service_b) worker_one = Process(name='Worker 1', target=worker) worker_two = Process(name='Worker 2', target=worker_1) serviceA.start() serviceA.join() serviceB.start() serviceB.join() worker_one.start() worker_one.join() worker_two.start() worker_two.join()

21.053571

62

0.557252

141

1,179

4.48227

0.205674

0.139241

0.113924

0.139241

0.435127

0

0.026461

0.230704

1,179

56

63

21.053571

0.670342

0

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0.111111

false

0

0.083333

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0.194444

0.222222

0

null

0

null

0

1

0

fad8908e9fc92fffbd4a6f5fdab2d8f88c32a5eb

1,238

py

Python

main.py

Goneiross/PDFtools

1625b422a1f5f0fc436c22d36d3d72d232d1c40c

[ "MIT" ]

null

main.py

Goneiross/PDFtools

1625b422a1f5f0fc436c22d36d3d72d232d1c40c

[ "MIT" ]

null

main.py

Goneiross/PDFtools

1625b422a1f5f0fc436c22d36d3d72d232d1c40c

[ "MIT" ]

null

import PyPDF4 import slate3k as slate import os import logging logging.propagate = False logging.getLogger().setLevel(logging.ERROR) def findWords(documentName) : with open(documentName, 'rb') as f: extracted_text = slate.PDF(f) with open("names.txt") as n: names = [name.rstrip() for name in n] pageNumber = len(extracted_text) print ("Document :", documentName) print ("Nombre de pages :", pageNumber) print("-------------------------------------------------") for name in names : pages = [] for pageIndex in range (0, len(extracted_text)) : if (extracted_text[pageIndex].find(name) != -1) : pages.append(pageIndex + 1) if (pages != []) : print ("Mot trouvé :", name) print (pages) f.close() n.close() def main(): documentNames = [] dir_path = os.path.dirname(os.path.realpath(__file__)) for root, dirs, files in os.walk(dir_path): for file in files: if file.endswith('.pdf'): print(file) documentNames.append(file) for documentName in documentNames : print ("Document :", documentName) findWords(documentName) main()

29.47619

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5.050725

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0

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42

63

29.47619

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0

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0.054054

false

0

0.108108

0

0.162162

0.189189

0

null

0

null

0

1

0

fad8e5dd6c90574808d1e135153f4d55747e2069

624

py

Python

demo.py

nspin/cloudpiercer

5bfe05f2950d62999db93e980de0c52b99fddfbe

[ "MIT" ]

2

2020-03-20T22:50:00.000Z

2020-06-12T21:13:37.000Z

demo.py

nspin/cloudpiercer

5bfe05f2950d62999db93e980de0c52b99fddfbe

[ "MIT" ]

8

2020-04-06T17:43:22.000Z

2022-02-17T08:25:06.000Z

demo.py

nspin/cloudpiercer

5bfe05f2950d62999db93e980de0c52b99fddfbe

[ "MIT" ]

1

2020-03-20T22:50:01.000Z

import asyncio from argparse import ArgumentParser from aiohttp import ClientSession from cloudpiercer import CloudPiercer SOLVER_ENDPOINT = 'http://localhost:8081/solve' def main(): parser = ArgumentParser() parser.add_argument('url') args = parser.parse_args() cloudpiercer = CloudPiercer(SOLVER_ENDPOINT) async def go(): async with ClientSession() as sess: resp, text = await cloudpiercer.fetch(sess, args.url, with_text=True) print(resp) print(text) loop = asyncio.get_event_loop() loop.run_until_complete(go()) if __name__ == '__main__': main()

24

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624

5.712329

0.561644

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0.1247

0

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0.206731

624

25

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0

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0

1

0.052632

false

0

0.210526

0

0.263158

0.105263

0

null

0

null

0

1

0

fadb4c879e499931d60f72ed2be27736908649d8

4,074

py

Python

pyramid_swagger/ingest.py

gchin/pyramid_swagger

97bd662e3731bda0e29677915457ec2e3b697495

[ "BSD-3-Clause" ]

null

pyramid_swagger/ingest.py

gchin/pyramid_swagger

97bd662e3731bda0e29677915457ec2e3b697495

[ "BSD-3-Clause" ]

null

pyramid_swagger/ingest.py

gchin/pyramid_swagger

97bd662e3731bda0e29677915457ec2e3b697495

[ "BSD-3-Clause" ]

null

# -*- coding: utf-8 -*- from __future__ import unicode_literals import os.path import simplejson from .load_schema import load_schema from .model import SwaggerSchema from .spec import validate_swagger_schemas API_DOCS_FILENAME = 'api_docs.json' class ResourceListingNotFoundError(Exception): pass class ApiDeclarationNotFoundError(Exception): pass def find_resource_names(api_docs_json): return [ api['path'].lstrip('/') for api in api_docs_json['apis'] ] def build_schema_mapping(schema_dir): """Discovers schema file locations and relations. :param schema_dir: the directory schema files live inside :type schema_dir: string :returns: A tuple of (resource listing filepath, mapping) where the mapping is between resource name and file path :rtype: (string, dict) """ def resource_name_to_filepath(name): return os.path.join(schema_dir, '{0}.json'.format(name)) listing, listing_json = _load_resource_listing(schema_dir) return ( listing, dict( (resource, resource_name_to_filepath(resource)) for resource in find_resource_names(listing_json) ) ) def _load_resource_listing(schema_dir): """Load the resource listing from file, handling errors. :param schema_dir: the directory schema files live inside :type schema_dir: string :returns: (resource listing filepath, resource listing json) """ resource_listing = os.path.join(schema_dir, API_DOCS_FILENAME) try: with open(resource_listing) as resource_listing_file: resource_listing_json = simplejson.load(resource_listing_file) # If not found, raise a more user-friendly error. except IOError: raise ResourceListingNotFoundError( 'No resource listing found at {0}. Note that your json file ' 'must be named {1}'.format(resource_listing, API_DOCS_FILENAME) ) return resource_listing, resource_listing_json def compile_swagger_schema(schema_dir, should_validate_schemas): """Build a SwaggerSchema from various files. :param schema_dir: the directory schema files live inside :type schema_dir: string :param should_validate_schemas: if True, check schemas for correctness :type should_validate_schemas: boolean :returns: a SwaggerSchema object """ listing, mapping = build_schema_mapping(schema_dir) schema_resolvers = ingest_resources( listing, mapping, schema_dir, should_validate_schemas, ) return SwaggerSchema( listing, mapping, schema_resolvers, ) def ingest_resources(listing, mapping, schema_dir, should_validate_schemas): """Consume the Swagger schemas and produce a queryable datastructure. :param listing: Filepath to a resource listing :type listing: string :param mapping: Map from resource name to filepath of its api declaration :type mapping: dict :param schema_dir: the directory schema files live inside :type schema_dir: string :param should_validate_schemas: if True, check schemas for correctness :type should_validate_schemas: boolean :returns: A list of SchemaAndResolver objects """ resource_filepaths = mapping.values() ingested_resources = [] for name, filepath in mapping.items(): try: ingested_resources.append(load_schema(filepath)) # If we have trouble reading any files, raise a more user-friendly # error. except IOError: raise ApiDeclarationNotFoundError( 'No api declaration found at {0}. Attempted to load the `{1}` ' 'resource relative to the schema_directory `{2}`. Perhaps ' 'your resource name and API declaration file do not ' 'match?'.format(filepath, name, schema_dir) ) if should_validate_schemas: validate_swagger_schemas( listing, resource_filepaths ) return ingested_resources

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null

0

null

0

1

0

fadd44baffd42de642862a0a9c740b7396f48977

4,038

py

Python

pxr/usd/plugin/usdAbc/testenv/testUsdAbcConversionHermiteCurves.py

DougRogers-DigitalFish/USD

d8a405a1344480f859f025c4f97085143efacb53

[ "BSD-2-Clause" ]

3,680

2016-07-26T18:28:11.000Z

2022-03-31T09:55:05.000Z

pxr/usd/plugin/usdAbc/testenv/testUsdAbcConversionHermiteCurves.py

DougRogers-DigitalFish/USD

d8a405a1344480f859f025c4f97085143efacb53

[ "BSD-2-Clause" ]

1,759

2016-07-26T19:19:59.000Z

2022-03-31T21:24:00.000Z

pxr/usd/plugin/usdAbc/testenv/testUsdAbcConversionHermiteCurves.py

DougRogers-DigitalFish/USD

d8a405a1344480f859f025c4f97085143efacb53

[ "BSD-2-Clause" ]

904

2016-07-26T18:33:40.000Z

2022-03-31T09:55:16.000Z

#!/pxrpythonsubst # # Copyright 2020 Pixar # # Licensed under the Apache License, Version 2.0 (the "Apache License") # with the following modification; you may not use this file except in # compliance with the Apache License and the following modification to it: # Section 6. Trademarks. is deleted and replaced with: # # 6. Trademarks. This License does not grant permission to use the trade # names, trademarks, service marks, or product names of the Licensor # and its affiliates, except as required to comply with Section 4(c) of # the License and to reproduce the content of the NOTICE file. # # You may obtain a copy of the Apache License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the Apache License with the above modification is # distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY # KIND, either express or implied. See the Apache License for the specific # language governing permissions and limitations under the Apache License. import unittest from pxr import Gf from pxr import Usd from pxr import UsdAbc from pxr import UsdGeom class TestUsdAbcConversionHermiteCurves(unittest.TestCase): @classmethod def setUpClass(cls): usdFile = 'original.usda' abcFile = 'converted.abc' UsdAbc._WriteAlembic(usdFile, abcFile) cls.stage = Usd.Stage.Open(abcFile) def _assertElementsAlmostEqual(self, seq1, seq2): self.assertTrue(all(Gf.IsClose(e1, e2, 1e-5) for e1, e2 in zip(seq1, seq2))) def _assertEmpty(self, sequence): self.assertFalse(sequence) def test_RoundTripHermite(self): time = Usd.TimeCode.EarliestTime() prim = self.stage.GetPrimAtPath('/Cubic/Ribbons/VaryingWidth') schema = UsdGeom.HermiteCurves(prim) # Interpolation metadata normalsInterpolation = schema.GetNormalsInterpolation() widthsInterpolation = schema.GetWidthsInterpolation() self.assertEqual(normalsInterpolation, UsdGeom.Tokens.varying) self.assertEqual(widthsInterpolation, UsdGeom.Tokens.varying) # These attributes may be varying time sampled curveVertexCounts = schema.GetCurveVertexCountsAttr().Get(time) points = schema.GetPointsAttr().Get(time) tangents = schema.GetTangentsAttr().Get(time) widths = schema.GetWidthsAttr().Get(time) normals = schema.GetNormalsAttr().Get(time) self._assertElementsAlmostEqual( points, [(0, 0, 0), (1, 1, 0), (2, 0, 0)]) self._assertElementsAlmostEqual( tangents, [(0, 1, 0), (1, 0, 0), (0, -1, 0)]) self._assertElementsAlmostEqual(widths, [0, .5, 0]) self._assertElementsAlmostEqual( normals, [(0, 0, 1), (0, 0, 1), (0, 0, 1)]) self.assertEqual(list(curveVertexCounts), [3]) def test_RoundTripHermiteWithVelocities(self): """Round tripping velocities is ambiguous""" time = Usd.TimeCode.EarliestTime() prim = self.stage.GetPrimAtPath('/Cubic/Tubes/WithVelocities') schema = UsdGeom.HermiteCurves(prim) # Interpolation metadata widthsInterpolation = schema.GetWidthsInterpolation() self.assertEqual(widthsInterpolation, UsdGeom.Tokens.varying) # These attributes may be varying time sampled curveVertexCounts = schema.GetCurveVertexCountsAttr().Get(time) points = schema.GetPointsAttr().Get(time) velocities = schema.GetVelocitiesAttr().Get(time) tangents = schema.GetTangentsAttr().Get(time) widths = schema.GetWidthsAttr().Get(time) self._assertElementsAlmostEqual(points, [(0, 0, 0), (1, 1, 0)]) self._assertElementsAlmostEqual(tangents, [(0, 1, 0), (1, 0, 0)]) self._assertElementsAlmostEqual(widths, [0, .5]) self._assertEmpty(velocities) self.assertEqual(list(curveVertexCounts), [2]) if __name__ == '__main__': unittest.main()

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null

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null

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fade7ee830d7c849f1246e21fdd7444efaa21bfb

3,218

py

Python

testsuite/tests/Q226-006__UT_bad_file_checker/run_test.py

AdaCore/style_checker

17108ebfc44375498063ecdad6c6e4430458e60a

[ "CNRI-Python" ]

2

2017-10-22T18:04:26.000Z

2020-03-06T11:07:41.000Z

testsuite/tests/Q226-006__UT_bad_file_checker/run_test.py

AdaCore/style_checker

17108ebfc44375498063ecdad6c6e4430458e60a

[ "CNRI-Python" ]

null

testsuite/tests/Q226-006__UT_bad_file_checker/run_test.py

AdaCore/style_checker

17108ebfc44375498063ecdad6c6e4430458e60a

[ "CNRI-Python" ]

4

2018-05-22T12:08:54.000Z

2020-12-14T15:25:27.000Z

import pytest def test_bad_file_checker(style_checker): """Check behavior when pep8 is missing """ style_checker.enable_unit_test() # Derive the TypificChecker class without providing the mandatory # methods which are otherwise abstract. from asclib.checkers.typific import TypificChecker, TypificCheckerInfo from asclib.checkers.rulific.all_checkers import ALL_RULIFIC_CHECKERS class BadFileChecker(TypificChecker): # Do provide a complete rulific_decision_map attribute, though, # as the contents of that dictionary is checked during # the object's initialization. rulific_decision_map = dict( (checker.RULE_CONFIG_NAME, False) for checker in ALL_RULIFIC_CHECKERS) # Same the typific_info attribute... typific_info = TypificCheckerInfo(comment_line_re='#', ada_RM_spec_p=False, copyright_box_r_edge_re=None) bad_checker = BadFileChecker('src/simple.py', None) # Now verify that calling those methods cause an exception. from asclib.checkers import FileCheckerError with pytest.raises(FileCheckerError) as cm: print(bad_checker.file_type) expected_output = \ 'abstract TypificChecker.file_type property unexpectedly called.' style_checker.assertOutputEqual(expected_output, str(cm.value)) with pytest.raises(FileCheckerError) as cm: bad_checker.run_external_checker() expected_output = \ 'abstract TypificChecker.run_external_checker method' \ ' unexpectedly called.' style_checker.assertOutputEqual(expected_output, str(cm.value)) def test_missing_entry_in_rulific_decision_map(style_checker): """Test when missing an entry in rulific_decision_map. """ style_checker.enable_unit_test() # Derive the TypificChecker class only providing some of # the mandatory overrides. In particular, only provide # an incomplete rulific_decision_map attribute, so as to # make sure we get an error when trying to instantiate # that broken class. # # We also need to provide the file_type attribute as it is used # to produce a human-readable error message. from asclib.checkers.typific import TypificChecker, TypificCheckerInfo from asclib.checkers.rulific.all_checkers import ALL_RULIFIC_CHECKERS class IncompleteFileChecker(TypificChecker): rulific_decision_map = dict( (checker.RULE_CONFIG_NAME, False) for checker in ALL_RULIFIC_CHECKERS[:-1]) typific_info = TypificCheckerInfo(comment_line_re='#', ada_RM_spec_p=False, copyright_box_r_edge_re=None) file_type = 'Python script' # Now verify that calling those methods cause a failed # assertion. with pytest.raises(AssertionError) as cm: IncompleteFileChecker('src/simple.py', None) expected_output = \ 'Python script checker missing config about' \ ' %s rule' % ALL_RULIFIC_CHECKERS[-1].RULE_CONFIG_NAME style_checker.assertOutputEqual(expected_output, str(cm.value))

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null

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null

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1

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fae0351c02946227f272f5e18e6321f3f13a21fe

10,980

py

Python

train.py

francois-rozet/amsi

0eb1bdfdf2fec37568fb03189d3a51cb794dcac8

[ "MIT" ]

1

2021-01-27T17:34:20.000Z

train.py

francois-rozet/amsi

0eb1bdfdf2fec37568fb03189d3a51cb794dcac8

[ "MIT" ]

null

train.py

francois-rozet/amsi

0eb1bdfdf2fec37568fb03189d3a51cb794dcac8

[ "MIT" ]

null

#!/usr/bin/env python import h5py import json import os import numpy as np import pandas as pd import torch import torch.nn as nn import torch.optim as optim from datetime import datetime from tqdm import tqdm from typing import List, Tuple import amnre from amnre.simulators.slcp import SLCP from amnre.simulators.gw import GW from amnre.simulators.hh import HH def build_embedding(input_size: torch.Size, arch: str = None, **kwargs) -> tuple: flatten = nn.Flatten(-len(input_size)) if arch == 'MLP': net = amnre.MLP(input_size.numel(), **kwargs) return nn.Sequential(flatten, net), net.output_size elif arch == 'ResNet': net = amnre.ResNet(input_size.numel(), **kwargs) return nn.Sequential(flatten, net), net.output_size else: return flatten, input_size.numel() def build_instance(settings: dict) -> tuple: # Simulator live = None if settings['simulator'] == 'GW': simulator = GW() if settings.get('live', True): live = simulator.noise elif settings['simulator'] == 'HH': simulator = HH() else: # settings['simulator'] == 'SCLP' simulator = SLCP() simulator.to(settings['device']) # Dataset if settings['samples'] is None: dataset = amnre.OnlineDataset(simulator, batch_size=settings['bs']) theta, x = simulator.joint() else: dataset = amnre.OfflineDataset(settings['samples'], batch_size=settings['bs'], device=settings['device'], live=live) theta, x = dataset[0] if theta is None: theta = simulator.prior.sample() theta_size = theta.numel() x_size = x.numel() # Moments if settings['weights'] is None: theta = simulator.prior.sample((2 ** 18,)) moments = torch.mean(theta, dim=0), torch.std(theta, dim=0) else: moments = torch.zeros(theta_size), torch.ones(theta_size) # Model & embedding embedding, x_size = build_embedding(x.shape, **settings['embedding']) model_args = settings['model'].copy() model_args['embedding'] = embedding model_args['moments'] = moments if settings['arbitrary']: model_args['hyper'] = settings['hyper'] model = amnre.AMNRE(theta_size, x_size, **model_args) else: masks = amnre.list2masks(settings['masks'], theta_size, settings['filter']) if len(masks) == 0: if settings['flow']: model = amnre.NPE(theta_size, x_size, prior=simulator.prior, **model_args) else: model = amnre.NRE(theta_size, x_size, **model_args) else: if settings['flow']: model = amnre.MNPE(masks, x_size, priors=[simulator.masked_prior(m) for m in masks], **model_args) else: model = amnre.MNRE(masks, x_size, **model_args) ## Weights if settings['weights'] is not None: weights = torch.load(settings['weights'], map_location='cpu') model.load_state_dict(weights) model.to(settings['device']) # Adversary if os.path.isfile(settings['adversary']) and type(model) in [amnre.NRE, amnre.MNRE]: adversary = load_model(settings['adversary']) adversary.to(settings['device']) adversary.eval() if type(adversary) in [amnre.NPE, amnre.MNPE]: adversary.ratio() if type(model) is amnre.MNRE: if type(adversary) in [amnre.MNRE, amnre.MNPE]: adversary.filter(model.masks) elif type(adversary) in [amnre.AMNRE]: adversary[model.masks] else: adversary = amnre.Dummy() for p in adversary.parameters(): p.requires_grad = False return simulator, dataset, model, adversary def load_settings(filename: str) -> dict: with open(filename) as f: settings = json.load(f) return settings def load_model(filename: str) -> nn.Module: settings = load_settings(filename.replace('.pth', '.json')) settings['weights'] = filename _, _, model, _ = build_instance(settings) return model if __name__ == '__main__': import argparse parser = argparse.ArgumentParser(description='Training') parser.add_argument('-device', default='cpu', choices=['cpu', 'cuda']) parser.add_argument('-simulator', default='SLCP', choices=['SLCP', 'GW', 'HH']) parser.add_argument('-samples', default=None, help='samples file (H5)') parser.add_argument('-live', default=False, action='store_true', help='live samples') # only GW parser.add_argument('-model', type=json.loads, default={}, help='model architecture') parser.add_argument('-hyper', type=json.loads, default=None, help='hypernet architecture') parser.add_argument('-embedding', type=json.loads, default={}, help='embedding architecture') parser.add_argument('-flow', default=False, action='store_true', help='normalizing flow') parser.add_argument('-arbitrary', default=False, action='store_true', help='arbitrary architecture') parser.add_argument('-masks', nargs='+', default=[], help='marginalzation masks') parser.add_argument('-filter', default=None, help='mask filter') parser.add_argument('-weights', default=None, help='warm-start weights') parser.add_argument('-criterion', default='NLL', choices=['NLL', 'FL', 'PL', 'QS'], help='optimization criterion') parser.add_argument('-adversary', default='notafile.pth', help='adversary network file (PTH)') parser.add_argument('-inverse', default=False, action='store_true', help='inverse adversary') parser.add_argument('-epochs', type=int, default=256, help='number of epochs') parser.add_argument('-descents', type=int, default=256, help='descents per epoch') parser.add_argument('-bs', type=int, default=1024, help='batch size') parser.add_argument('-lr', type=float, default=1e-3, help='initial learning rate') parser.add_argument('-wd', type=float, default=1e-4, help='weight decay') parser.add_argument('-amsgrad', type=bool, default=False, help='AMS gradient') parser.add_argument('-scheduler', default='plateau', choices=['plateau', 'exp', 'cosine'], help='learning rate scheduler') parser.add_argument('-patience', type=int, default=7, help='scheduler patience') parser.add_argument('-threshold', type=float, default=1e-2, help='scheduler threshold') parser.add_argument('-factor', type=float, default=5e-1, help='scheduler factor') parser.add_argument('-min-lr', type=float, default=1e-6, help='minimum learning rate') parser.add_argument('-clip', type=float, default=1e1, help='gradient norm') parser.add_argument('-valid', default=None, help='validation samples file (H5)') parser.add_argument('-o', '--output', default='products/models/out.pth', help='output file (PTH)') args = parser.parse_args() args.date = datetime.now().strftime(r'%Y-%m-%d %H:%M:%S') # Output directory if os.path.dirname(args.output): os.makedirs(os.path.dirname(args.output), exist_ok=True) # Simulator & Model settings = vars(args) simulator, dataset, model, adversary = build_instance(settings) ## Arbitrary masks if args.arbitrary: theta_size = simulator.prior.sample().numel() if not args.masks: args.masks.append('uniform') if args.masks[0] == 'poisson': mask_sampler = amnre.PoissonMask(theta_size, args.filter) elif args.masks[0] == 'uniform': mask_sampler = amnre.UniformMask(theta_size, args.filter) else: masks = amnre.list2masks(args.masks, theta_size, args.filter) mask_sampler = amnre.SelectionMask(masks) mask_sampler.to(args.device) else: mask_sampler = None # Criterion(s) if args.flow: criterion = amnre.NLL() elif args.criterion == 'FL': criterion = amnre.FocalWithLogitsLoss() elif args.criterion == 'PL': criterion = amnre.PeripheralWithLogitsLoss() elif args.criterion == 'QS': criterion = amnre.QSWithLogitsLoss() else: # args.criterion == 'NLL': criterion = amnre.NLLWithLogitsLoss() # Optimizer & Scheduler optimizer = optim.AdamW( model.parameters(), lr=args.lr, weight_decay=args.wd, amsgrad=args.amsgrad, ) if args.scheduler == 'cosine': scheduler = amnre.CosineAnnealingLR( optimizer, T_max=args.epochs, eta_min=args.min_lr, ) elif args.scheduler == 'exp': scheduler = amnre.ExponentialLR( optimizer, gamma=(args.lr / args.min_lr) ** (-1 / args.epochs), ) else: # args.scheduler == 'plateau': scheduler = amnre.ReduceLROnPlateau( optimizer, factor=args.factor, patience=args.patience, threshold=args.threshold, min_lr=args.min_lr, ) # Datasets trainset = amnre.LTEDataset(dataset) if args.valid is not None: validset = amnre.OfflineDataset(args.valid, batch_size=args.bs, device=args.device) validset = amnre.LTEDataset(validset) # Training stats = [] for epoch in tqdm(range(1, args.epochs + 1)): model.train() duration, losses = amnre.routine( model, trainset, criterion, optimizer=optimizer, adversary=adversary, inverse=args.inverse, descents=args.descents, flow=args.flow, mask_sampler=mask_sampler, clip=args.clip, ) stats.append({ 'epoch': epoch, 'time': duration, 'lr': scheduler.lr, 'mean': losses.mean(dim=0).tolist(), 'std': losses.std(dim=0).tolist(), }) if args.valid is not None: with torch.no_grad(): model.eval() _, v_losses = amnre.routine( model, validset, criterion, optimizer=None, adversary=adversary, inverse=args.inverse, flow=args.flow, mask_sampler=mask_sampler, ) stats[-1].update({ 'v_mean': v_losses.mean(dim=0).tolist(), 'v_std': v_losses.std(dim=0).tolist(), }) scheduler.step(v_losses.mean()) else: scheduler.step(losses.mean()) df = pd.DataFrame(stats) df.to_csv(args.output.replace('.pth', '.csv'), index=False) if scheduler.bottom: break # Outputs ## Weights if hasattr(model, 'clear'): model.clear() torch.save(model.cpu().state_dict(), args.output) ## Settings with open(args.output.replace('.pth', '.json'), 'w') as f: json.dump(settings, f, indent=4)

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null

0

null

0

1

0

fae1a1fa4ccde0721ac41c806e9039d82ded972b

1,039

py

Python

templates and examples/announcements example/GET_announcements_example.py

elmiguel/Bb_rest_helper

15b951bd629586fd672ddb4a9e68c29fb4e77709

[ "BSD-3-Clause" ]

null

templates and examples/announcements example/GET_announcements_example.py

elmiguel/Bb_rest_helper

15b951bd629586fd672ddb4a9e68c29fb4e77709

[ "BSD-3-Clause" ]

null

templates and examples/announcements example/GET_announcements_example.py

elmiguel/Bb_rest_helper

15b951bd629586fd672ddb4a9e68c29fb4e77709

[ "BSD-3-Clause" ]

null

#imports from Bb_rest_helper import Get_Config from Bb_rest_helper import Auth_Helper from Bb_rest_helper import Bb_Requests from Bb_rest_helper import Bb_Utils def main(): #Initialize an instance of the Get_Config class, passing the file path of the configuration file as argument. config=Get_Config("./learn_config.json") #Get configration values from config.json. url=config.get_url() key=config.get_key() secret=config.get_secret() #Set logging utils= Bb_Utils() utils.set_logging() #Authentication auth=Auth_Helper(url,key,secret) token=auth.learn_auth() #Prepare the request GET_announcements_endpoint=f'{url}/learn/api/public/v1/announcements' params={ "limit":"10", "fields":"id,title,body" } #request req= Bb_Requests() GET_announcements=req.Bb_GET(GET_announcements_endpoint,token,params) #Pretty print results to the console. utils.pretty_printer(GET_announcements) if __name__ == "__main__": main()

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null

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fae3a6d7d10b9c8097dabd75c90ca0b71987eba6

1,376

py

Python

numba/roc/tests/hsapy/test_positioning.py

tolysz/numba

d7953a18dbf5ea231dc16e967ce8e9b754578ea6

[ "Apache-2.0", "BSD-2-Clause" ]

null

numba/roc/tests/hsapy/test_positioning.py

tolysz/numba

d7953a18dbf5ea231dc16e967ce8e9b754578ea6

[ "Apache-2.0", "BSD-2-Clause" ]

1

2019-02-11T13:46:30.000Z

numba/roc/tests/hsapy/test_positioning.py

asodeur/numba

d7953a18dbf5ea231dc16e967ce8e9b754578ea6

[ "Apache-2.0", "BSD-2-Clause" ]

null

import numpy as np from numba import roc import numba.unittest_support as unittest class TestPositioning(unittest.TestCase): def test_kernel_jit(self): @roc.jit def udt(output): global_id = roc.get_global_id(0) global_size = roc.get_global_size(0) local_id = roc.get_local_id(0) group_id = roc.get_group_id(0) num_groups = roc.get_num_groups(0) workdim = roc.get_work_dim() local_size = roc.get_local_size(0) output[0, group_id, local_id] = global_id output[1, group_id, local_id] = global_size output[2, group_id, local_id] = local_id output[3, group_id, local_id] = local_size output[4, group_id, local_id] = group_id output[5, group_id, local_id] = num_groups output[6, group_id, local_id] = workdim out = np.zeros((7, 2, 3), dtype=np.intp) udt[2, 3](out) np.testing.assert_equal([[0, 1, 2], [3, 4, 5]], out[0]) np.testing.assert_equal(6, out[1]) np.testing.assert_equal([[0, 1, 2]] * 2, out[2]) np.testing.assert_equal(3, out[3]) np.testing.assert_equal([[0, 0, 0], [1, 1, 1]], out[4]) np.testing.assert_equal(2, out[5]) np.testing.assert_equal(1, out[6]) if __name__ == '__main__': unittest.main()

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0.1875

0

null

0

null

0

1

0

fae926464e0fc8eaff483735b546273b89790232

6,563

py

Python

sprocket/model/diagGMM.py

kan-bayashi/sprocket

b87e1aa3822ed23019b5373660e0f977cbc6c996

[ "MIT" ]

3

2020-06-03T08:29:49.000Z

2022-03-23T02:29:01.000Z

sprocket/model/diagGMM.py

kan-bayashi/sprocket

b87e1aa3822ed23019b5373660e0f977cbc6c996

[ "MIT" ]

1

2020-06-07T23:06:10.000Z

sprocket/model/diagGMM.py

kan-bayashi/sprocket

b87e1aa3822ed23019b5373660e0f977cbc6c996

[ "MIT" ]

1

2020-06-03T09:41:42.000Z

# -*- coding: utf-8 -*- import numpy as np import sklearn.mixture from sklearn.mixture.gaussian_mixture import _compute_precision_cholesky class BlockDiagonalGaussianMixture(sklearn.mixture.GaussianMixture): """GMM with block diagonal covariance matrix This class offers the training of GMM with block diagonal covariance matrix. Note that the parent class (GaussianMixture) is trained as full-covariance matrix Parameters ---------- n_mix : int, optional The number of mixture components of the GMM Default set to 32. n_iter : int, optional The number of iteration for EM algorithm. Default set to 100. floor : str, optional Flooring of covariance matrix Attributes ---------- param : Sklean-based model parameters of the GMM """ def __init__(self, n_mix=32, n_iter=100, floor=1e-6): super().__init__(n_components=n_mix, reg_covar=floor, max_iter=n_iter, covariance_type='full') self.n_mix = n_mix self.n_iter = n_iter self.floor = floor # seed for random in sklearn self.random_state = np.random.mtrand._rand def fit(self, X): """Fit GMM parameters to X Parameters ---------- X : array-like, shape (n_samples, n_features) """ # initialize self._initialize_parameters(X, self.random_state) lower_bound = -np.infty for n in range(self.n_iter): # E-step log_prob_norm, log_resp = self._e_step(X) # M-step self._m_step(X, log_resp) # check convergence back_lower_bound = lower_bound lower_bound = self._compute_lower_bound( log_resp, log_prob_norm) def _m_step(self, X, log_resp): """M step. Parameters ---------- X : array-like, shape (n_samples, n_features) log_resp : array-like, shape (n_samples, n_components) Logarithm of the posterior probabilities (or responsibilities) of the point of each sample in X. """ n_samples, _ = X.shape self.weights_, self.means_, self.covariances_ = ( self._estimate_gaussian_parameters(X, np.exp(log_resp), self.reg_covar, self.covariance_type)) self.weights_ /= n_samples self.precisions_cholesky_ = _compute_precision_cholesky( self.covariances_, self.covariance_type) def _estimate_gaussian_parameters(self, X, resp, reg_covar, covariance_type): """Estimate the Gaussian distribution parameters. Parameters ---------- X : array-like, shape (n_samples, n_features) The input data array. resp : array-like, shape (n_samples, n_components) The responsibilities for each data sample in X. reg_covar : float The regularization added to the diagonal of the covariance matrices. covariance_type : {'full', 'tied', 'diag', 'spherical'} The type of precision matrices. Returns ------- nk : array-like, shape (n_components,) The numbers of data samples in the current components. means : array-like, shape (n_components, n_features) The centers of the current components. covariances : array-like (n_components, n_features, n_features) The covariance matrix of the current components. The shape depends of the covariance_type. """ # estimate weight and mean nk = resp.sum(axis=0) + 10 * np.finfo(resp.dtype).eps means = np.dot(resp.T, X) / nk[:, np.newaxis] # estimate covariance n_components, n_features = means.shape diagcov = self._calculate_diag_covariances(resp, nk, X, X, means, means) xycov = self._calculate_diag_covariances(resp, nk, X[:, :n_features // 2], X[:, n_features // 2:], means[:, :n_features // 2], means[:, n_features // 2:]) # block_diag to full covariances = self._block_diag_to_full(diagcov, xycov) return nk, means, covariances def _block_diag_to_full(self, diagcov, xycov): """Transform diagonal covariance to full covariance Parameters ---------- diagcov : array-like, shape (n_components, n_features) Diagonal covariance xycov : array-like, shape (n_components, n_features // 2) Variance-covariance Returns ------- covariance : array-like, shape (n_components, n_features, n_features) Full covariance consiting of xxcov, xycov, yxcov, yycov """ n_components, n_features = diagcov.shape covariances = np.empty((n_components, n_features, n_features)) for m in range(n_components): covariances[m] = np.diag(diagcov[m]) covariances[m, n_features // 2:, :n_features // 2] = np.diag(xycov[m]) covariances[m, :n_features // 2, n_features // 2:] = np.diag(xycov[m]) return covariances def _calculate_diag_covariances(self, resp, nk, x, y, xmeans, ymeans): """Calculate diagonal covariance in each portion Parameters ---------- resp : array-like, shape (n_samples, n_components) The responsibilities for each data sample in X. nk : array-like, shape (n_components,) The numbers of data samples in the current components. x, y : array-like, shape (n_samples, n_features) The input data array of source and atarget. xmeans, ymeans : array-like, shape (n_components, n_features) Mean of x and y Returns ------- diag_covariances : array-like, shape (n_components, n_features) """ avg_XY = np.dot(resp.T, x * y) / nk[:, np.newaxis] avg_xymeans = xmeans * ymeans avg_x_ymeans = ymeans * np.dot(resp.T, x) / nk[:, np.newaxis] avg_y_xmeans = xmeans * np.dot(resp.T, y) / nk[:, np.newaxis] diag_covariances = avg_XY - \ (avg_x_ymeans + avg_y_xmeans) + avg_xymeans + self.floor return diag_covariances

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null

0

null

0

1

0

faea7e9591c0e3b9bf385325c1b31a252b6e8852

1,168

py

Python

fft/first_idft.py

mherbert7/dsp

611da522ff2c659bf4e8d1f124999ed39937f9d9

[ "Unlicense" ]

null

fft/first_idft.py

mherbert7/dsp

611da522ff2c659bf4e8d1f124999ed39937f9d9

[ "Unlicense" ]

null

fft/first_idft.py

mherbert7/dsp

611da522ff2c659bf4e8d1f124999ed39937f9d9

[ "Unlicense" ]

null

# -*- coding: utf-8 -*- """ Created on Wed Apr 11 23:31:51 2018 @author: Marcus """ import numpy as np import time N = 2**4 samples = np.random.normal(0, 1, N) + 1j * np.random.normal(0, 1, N) pre_calc = np.zeros(N, dtype=np.complex) pre_m = np.zeros((N, N), dtype=np.complex) for n in range(N): pre_calc[n] = (1j * 2 * np.pi * n) / N for m in range(N): pre_m[m] = np.exp(pre_calc * m) def my_idft(x, pre_calc_vals): N = len(x) output = np.zeros(N, dtype=np.complex) for m in range(N): intermediate_sum = 0 for n in range(N): intermediate_sum += x[n] * pre_calc_vals[m][n] output[m] = intermediate_sum return output / N my_t0 = time.clock() my_result = my_idft(samples, pre_m) my_t1 = time.clock() np_t0 = time.clock() np_result = np.fft.ifft(samples) np_t1 = time.clock() if(np.allclose(my_result, np_result)): print("Results match!") else: print("Error: Results do not match!") my_time = my_t1 - my_t0 np_time = np_t1 - np_t0 print("My IDFT:", my_time, "s") print("NP IDFT:", np_time, "s") print("NP is", my_time / np_time, "times faster.")

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1,168

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20.491228

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0.147059

0

null

0

null

0

1

0

faeab8b66f91683aa6f90e52431fcd0991331853

3,348

py

Python

shambler.py

bruno-chavez/shambler

5ec0fac191b332938fd0ce9f94b55b9b4ba5f762

[ "MIT" ]

null

shambler.py

bruno-chavez/shambler

5ec0fac191b332938fd0ce9f94b55b9b4ba5f762

[ "MIT" ]

1

2018-04-17T03:09:46.000Z

2018-04-27T11:38:54.000Z

shambler.py

bruno-chavez/shambler

5ec0fac191b332938fd0ce9f94b55b9b4ba5f762

[ "MIT" ]

2

2018-04-26T05:09:09.000Z

2018-11-02T18:50:41.000Z

import os from itertools import chain JSON_EXT = '.json' JSON_FOLDER = 'JSON_Files' def shambler(source_file, target_file_path, json_key): # Checking whether the user has input file names, file paths # If they are relative, we will let python take care of it. source_file = _resolve_path(source_file) if not os.path.exists(source_file): raise FileNotFoundError('%s was not found.' % source_file) target_file_path = _resolve_path(target_file_path, extension=JSON_EXT) # Places all lines of original file in a list, checks for correct file name. source_file_lines = [] with open(source_file, 'r') as f: source_file_lines = f.readlines() # Replaces all the double quotes to single quotes and strips trailing whitespace and removes empty lines source_file_lines = [line.rstrip().replace('\"', '\'') for line in source_file_lines] source_file_lines = [line for line in source_file_lines if line] with open(target_file_path, 'w') as json_file: json_file.write("[\n") num_lines = len(source_file_lines) keys = _resolve_key_list(json_key, num_lines) for i, (line, key) in enumerate(zip(source_file_lines, keys)): json_file.write("\t{\n") json_file.write('\t"{K}": "{V}"\n'.format(K=key, V=line)) json_file.write('\t}\n' if i == num_lines - 1 else '\t},\n') json_file.write("]") return target_file_path def shambler_interactive(): # Necessary inputs for shambler to work with. file_path = input("Enter your input plain text file: ") json_file_name = input("Enter a name for the output JSON file: ") # User has the option of inputting json_key in the format "key, number, key, number" # So that they can specify keys and the number of uses of each key in order. json_key = input("Enter a key to use in the JSON file: ") output_file = shambler(file_path, json_file_name, json_key) print("%s created successfully." % output_file) def _resolve_key_list(json_key_user_input, num_lines): if ',' in json_key_user_input: # Split user input by commas keys = json_key_user_input.strip().split(',') keys = zip(keys[::2], keys[1::2]) # Creating a list of each key entry multiplied by the number following it. # Added max in case user enters negative/zero value to default to 1 keys = list(chain.from_iterable( [[key_pair[0]] * max(int(key_pair[1]), 1) for key_pair in keys])) # Now filling out the list with the last entry if it does not match the number of lines in the source file. if len(keys) < num_lines: keys = keys + [keys[-1]] * abs(len(keys) - num_lines) else: keys = [json_key_user_input] * num_lines return keys def _resolve_path(file_path, extension=''): if not file_path: raise IOError('Missing input. Please try again.') script_path = os.path.abspath(os.path.dirname(os.path.realpath(__file__))) # Resolves the json file path to an absolute path within the JSON_Files folder if not already a path if os.path.sep not in file_path: file_path = os.path.join(script_path, JSON_FOLDER, file_path) return file_path + extension if __name__ == '__main__': shambler_interactive()

38.930233

115

0.670848

512

3,348

4.164063

0.3125

0.070356

0.056285

0.030019

0.142589

0.045028

0

0.003499

0.23178

3,348

85

116

39.388235

0.825428

0.258961

0

0.039216

0.101297

0

1

0.078431

false

0

0.039216

0

0.176471

0.019608

0

null

0

null

0

1

0

faec0617cec52d2af368906d78d2dfaae3d250b7

5,920

py

Python

ionoscloud/models/kubernetes_auto_scaling.py

ionos-cloud/ionos-cloud-sdk-python

3c5804697c262898e6f6a438dc40e1b45a4bb5c9

[ "Apache-2.0" ]

null

ionoscloud/models/kubernetes_auto_scaling.py

ionos-cloud/ionos-cloud-sdk-python

3c5804697c262898e6f6a438dc40e1b45a4bb5c9

[ "Apache-2.0" ]

null

ionoscloud/models/kubernetes_auto_scaling.py

ionos-cloud/ionos-cloud-sdk-python

3c5804697c262898e6f6a438dc40e1b45a4bb5c9

[ "Apache-2.0" ]

null

# coding: utf-8 """ CLOUD API IONOS Enterprise-grade Infrastructure as a Service (IaaS) solutions can be managed through the Cloud API, in addition or as an alternative to the \"Data Center Designer\" (DCD) browser-based tool. Both methods employ consistent concepts and features, deliver similar power and flexibility, and can be used to perform a multitude of management tasks, including adding servers, volumes, configuring networks, and so on. # noqa: E501 The version of the OpenAPI document: 6.0 Generated by: https://openapi-generator.tech """ import pprint import re # noqa: F401 import six from ionoscloud.configuration import Configuration class KubernetesAutoScaling(object): """NOTE: This class is auto generated by OpenAPI Generator. Ref: https://openapi-generator.tech Do not edit the class manually. """ """ Attributes: openapi_types (dict): The key is attribute name and the value is attribute type. attribute_map (dict): The key is attribute name and the value is json key in definition. """ openapi_types = { 'min_node_count': 'int', 'max_node_count': 'int', } attribute_map = { 'min_node_count': 'minNodeCount', 'max_node_count': 'maxNodeCount', } def __init__(self, min_node_count=None, max_node_count=None, local_vars_configuration=None): # noqa: E501 """KubernetesAutoScaling - a model defined in OpenAPI""" # noqa: E501 if local_vars_configuration is None: local_vars_configuration = Configuration() self.local_vars_configuration = local_vars_configuration self._min_node_count = None self._max_node_count = None self.discriminator = None self.min_node_count = min_node_count self.max_node_count = max_node_count @property def min_node_count(self): """Gets the min_node_count of this KubernetesAutoScaling. # noqa: E501 The minimum number of worker nodes that the managed node group can scale in. Should be set together with 'maxNodeCount'. Value for this attribute must be greater than equal to 1 and less than equal to maxNodeCount. # noqa: E501 :return: The min_node_count of this KubernetesAutoScaling. # noqa: E501 :rtype: int """ return self._min_node_count @min_node_count.setter def min_node_count(self, min_node_count): """Sets the min_node_count of this KubernetesAutoScaling. The minimum number of worker nodes that the managed node group can scale in. Should be set together with 'maxNodeCount'. Value for this attribute must be greater than equal to 1 and less than equal to maxNodeCount. # noqa: E501 :param min_node_count: The min_node_count of this KubernetesAutoScaling. # noqa: E501 :type min_node_count: int """ if self.local_vars_configuration.client_side_validation and min_node_count is None: # noqa: E501 raise ValueError("Invalid value for `min_node_count`, must not be `None`") # noqa: E501 self._min_node_count = min_node_count @property def max_node_count(self): """Gets the max_node_count of this KubernetesAutoScaling. # noqa: E501 The maximum number of worker nodes that the managed node pool can scale-out. Should be set together with 'minNodeCount'. Value for this attribute must be greater than equal to 1 and minNodeCount. # noqa: E501 :return: The max_node_count of this KubernetesAutoScaling. # noqa: E501 :rtype: int """ return self._max_node_count @max_node_count.setter def max_node_count(self, max_node_count): """Sets the max_node_count of this KubernetesAutoScaling. The maximum number of worker nodes that the managed node pool can scale-out. Should be set together with 'minNodeCount'. Value for this attribute must be greater than equal to 1 and minNodeCount. # noqa: E501 :param max_node_count: The max_node_count of this KubernetesAutoScaling. # noqa: E501 :type max_node_count: int """ if self.local_vars_configuration.client_side_validation and max_node_count is None: # noqa: E501 raise ValueError("Invalid value for `max_node_count`, must not be `None`") # noqa: E501 self._max_node_count = max_node_count def to_dict(self): """Returns the model properties as a dict""" result = {} for attr, _ in six.iteritems(self.openapi_types): value = getattr(self, attr) if isinstance(value, list): result[attr] = list(map( lambda x: x.to_dict() if hasattr(x, "to_dict") else x, value )) elif hasattr(value, "to_dict"): result[attr] = value.to_dict() elif isinstance(value, dict): result[attr] = dict(map( lambda item: (item[0], item[1].to_dict()) if hasattr(item[1], "to_dict") else item, value.items() )) else: result[attr] = value return result def to_str(self): """Returns the string representation of the model""" return pprint.pformat(self.to_dict()) def __repr__(self): """For `print` and `pprint`""" return self.to_str() def __eq__(self, other): """Returns true if both objects are equal""" if not isinstance(other, KubernetesAutoScaling): return False return self.to_dict() == other.to_dict() def __ne__(self, other): """Returns true if both objects are not equal""" if not isinstance(other, KubernetesAutoScaling): return True return self.to_dict() != other.to_dict()

37.707006

438

0.651689

770

5,920

4.823377

0.232468

0.101777

0.067851

0.03231

0.542811

0.512924

0.506193

0.400108

0.378568

0.303177

0

0.014839

0.271453

5,920

156

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37.948718

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false

0

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0.371429

0.028571

0

null

0

null

0

1

0

faec3f0c82aaf05f81a0627d474207469cf7fa10

1,169

py

Python

Leetcode/Python Solutions/Dynamic Programming/UniqueBinarySearchTrees.py

Mostofa-Najmus-Sakib/Applied-Algorithm

bc656fd655617407856e0ce45b68585fa81c5035

[ "MIT" ]

1

2020-01-06T02:21:56.000Z

Leetcode/Python Solutions/Dynamic Programming/UniqueBinarySearchTrees.py

Mostofa-Najmus-Sakib/Applied-Algorithm

bc656fd655617407856e0ce45b68585fa81c5035

[ "MIT" ]

null

Leetcode/Python Solutions/Dynamic Programming/UniqueBinarySearchTrees.py

Mostofa-Najmus-Sakib/Applied-Algorithm

bc656fd655617407856e0ce45b68585fa81c5035

[ "MIT" ]

3

2021-02-22T17:41:01.000Z

2022-01-13T05:03:19.000Z

""" LeetCode Problem: 96. Unique Binary Search Trees Link: https://leetcode.com/problems/unique-binary-search-trees/ Video Link: https://www.youtube.com/watch?v=CMaZ69P1bAc Resources: https://en.wikipedia.org/wiki/Catalan_number Written by: Mostofa Adib Shakib Language: Python For Catalan(3) Catalan(2) / \ Catalan(1) Catalan(1) (LST) (RST) LeftSubTree(LST): Value increases upto the given catalan number RightSubTree(RST): Value decreases until 0 """ # Dynamic Programming # Time Complexity: O(n*m) # Space Complexity: O(n) class Solution: def numTrees(self, n: int) -> int: # Assume the 0th case # The catalan number for Catalan(1) = 1 dp = [1, 1] + [0] * (n-1) for i in range(2, n+1): # The outer loop makes every number the root and calculates it's catalan number for j in range(1, i+1): # The inner calculates the summation of the cartesian product for the given catalan number dp[i] += dp[j-1] * dp[i-j] # The left value increases where as the right value decreases return dp[-1]

31.594595

142

0.620188

165

1,169

4.387879

0.533333

0.089779

0.049724

0.063536

0

0.027219

0.27716

1,169

37

143

31.594595

0.829586

0.717707

0

1

0.142857

false

0

0.428571

0

null

0

null

0

1

0

faeee7998bd569d00da26eb3e5219811d8da8001

2,252

py

Python

shellpython/shellpy.py

wujuguang/shellpy

54a41b7bfc6a7b6b10fd19419ce058bddd96e5bd

[ "BSD-3-Clause" ]

706

2016-01-18T12:45:55.000Z

2022-03-31T05:06:14.000Z

shellpython/shellpy.py

wujuguang/shellpy

54a41b7bfc6a7b6b10fd19419ce058bddd96e5bd

[ "BSD-3-Clause" ]

61

2016-01-17T08:08:40.000Z

2022-02-13T19:18:01.000Z

shellpython/shellpy.py

wujuguang/shellpy

54a41b7bfc6a7b6b10fd19419ce058bddd96e5bd

[ "BSD-3-Clause" ]

78

2016-02-13T14:56:33.000Z

2022-03-15T22:01:16.000Z

#!/usr/bin/env python import sys import os import re import subprocess import shellpython.config as config from shellpython.preprocessor import preprocess_file from argparse import ArgumentParser from shellpython.constants import * def main2(): main(python_version=2) def main3(): main(python_version=3) def main(python_version): custom_usage = '''%(prog)s [SHELLPY ARGS] file [SCRIPT ARGS] For arguments help use: %(prog)s --help ''' custom_epilog = '''github : github.com/lamerman/shellpy''' try: spy_file_index = next(index for index, arg in enumerate(sys.argv) if re.match('.+\.spy$', arg)) shellpy_args = sys.argv[1:spy_file_index] script_args = sys.argv[spy_file_index + 1:] except StopIteration: shellpy_args = sys.argv[1:] spy_file_index = None parser = ArgumentParser(description='A tool for convenient shell scripting in python', usage=custom_usage, epilog=custom_epilog) parser.add_argument('-v', '--verbose', help='increase output verbosity. Always print the command being executed', action="store_true") parser.add_argument('-vv', help='even bigger output verbosity. All stdout and stderr of executed commands is ' 'printed', action="store_true") shellpy_args, _ = parser.parse_known_args(shellpy_args) if spy_file_index is None: exit('No *.spy file was specified. Only *.spy files are supported by the tool.') if shellpy_args.verbose or shellpy_args.vv: config.PRINT_ALL_COMMANDS = True if shellpy_args.vv: config.PRINT_STDOUT_ALWAYS = True config.PRINT_STDERR_ALWAYS = True filename = sys.argv[spy_file_index] processed_file = preprocess_file(filename, is_root_script=True, python_version=python_version) # include directory of the script to pythonpath new_env = os.environ.copy() new_env['PYTHONPATH'] = new_env.get("PYTHONPATH", '') + os.pathsep + os.path.dirname(filename) new_env[SHELLPY_PARAMS] = config.dumps() retcode = subprocess.call(processed_file + ' ' + ' '.join(script_args), shell=True, env=new_env) exit(retcode) if __name__ == '__main__': main()

32.171429

117

0.682504

295

2,252

4.99661

0.410169

0.059701

0.048847

0.024423

0.100407

0.042062

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2,252

69

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32.637681

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0.229167

0.041667

0

null

0

null

0

1

0

faef057ae695587c91a977897400ae054181fd10

3,833

py

Python

tordatahub/auth/AliyunAccount.py

jasonz93/python-tordatahub

3a9a497d5a0bebf915d7e24049dd8b06099e3c04

[ "Apache-2.0" ]

null

tordatahub/auth/AliyunAccount.py

jasonz93/python-tordatahub

3a9a497d5a0bebf915d7e24049dd8b06099e3c04

[ "Apache-2.0" ]

null

tordatahub/auth/AliyunAccount.py

jasonz93/python-tordatahub

3a9a497d5a0bebf915d7e24049dd8b06099e3c04

[ "Apache-2.0" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- # Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. The ASF licenses this file # to you under the Apache License, Version 2.0 (the # "License"); you may not use this file except in compliance # with the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY # KIND, either express or implied. See the License for the # specific language governing permissions and limitations # under the License. from __future__ import absolute_import import hmac import base64 from hashlib import sha1 from collections import OrderedDict from ..thirdparty import six from ..thirdparty.six.moves.urllib.parse import urlparse, unquote from ..models import Headers from ..utils import Logger, hmac_sha1 from .core import Account, AccountType class AliyunAccount(Account): """ Aliyun account implement base from :class:`tordatahub.auth.Account` """ __slots__ = '_access_id', '_access_key' def __init__(self, *args, **kwds): self._access_id = kwds.get('access_id', '') self._access_key = kwds.get('access_key', '') super(AliyunAccount, self).__init__(*args, **kwds) @property def access_id(self): return self._access_id @access_id.setter def access_id(self, value): self._access_id = value @property def access_key(self): return self._access_key @access_key.setter def access_key(self, value): self._access_key = value def get_type(self): """ Get account type. :return: the account type :rtype: :class:`datahub.auth.AccountType` """ return AccountType.ALIYUN def _build_canonical_str(self, url_components, req): # Build signing string lines = [req.method, req.headers[Headers.CONTENT_TYPE], req.headers[Headers.DATE], ] headers_to_sign = dict() # req headers headers = req.headers for k, v in six.iteritems(headers): k = k.lower() if k.startswith('x-datahub-'): headers_to_sign[k] = v # url params if url_components.query: params_list = sorted(parse_qsl(url_components.query, True), key=lambda it: it[0]) params = dict(params_list) for k, v in params: if key.startswith('x-datahub-'): headers_to_sign[k] = v headers_to_sign = OrderedDict([(k, headers_to_sign[k]) for k in sorted(headers_to_sign)]) Logger.logger.debug('headers to sign: %s' % headers_to_sign) for k, v in six.iteritems(headers_to_sign): lines.append('%s:%s' % (k, v)) lines.append(url_components.path) return '\n'.join(lines) def sign_request(self, req, endpoint): """ Generator signature for request. :param req: request object :param endpoint: tordatahub server endpoint :return: none """ url = req.url[len(endpoint):] url_components = urlparse(unquote(url)) canonical_str = self._build_canonical_str(url_components, req) Logger.logger.debug('canonical string: ' + canonical_str) sign = hmac_sha1(self._access_key, canonical_str).decode() auth_str = 'DATAHUB %s:%s' %(self._access_id, sign) req.headers[Headers.AUTHORIZATION] = auth_str

31.941667

92

0.642317

487

3,833

4.87885

0.35729

0.030303

0.049242

0.008838

0.049663

0.027778

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3,833

119

93

32.210084

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0.133333

false

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0.033333

0.4

0

null

0

null

0

1

0

faef3e33175a1546c06dd449b662bb75945e709e

963

py

Python

Searching/binary_maze.py

kimjiwook0129/Coding-Interivew-Cheatsheet

574e6acecdb617b9c3cef7ec3b154ab183d8b99a

[ "MIT" ]

3

2022-01-09T04:33:04.000Z

2022-02-04T17:40:43.000Z

Searching/binary_maze.py

kimjiwook0129/Coding-Interivew-Cheatsheet

574e6acecdb617b9c3cef7ec3b154ab183d8b99a

[ "MIT" ]

null

Searching/binary_maze.py

kimjiwook0129/Coding-Interivew-Cheatsheet

574e6acecdb617b9c3cef7ec3b154ab183d8b99a

[ "MIT" ]

null

# 이것이 코딩테스트다 p.152 # Sample Input: # 5 6 # 101010 # 111111 # 000001 # 111111 # 111111 # Output : 10 from collections import deque # Complexities : Time O(NM) | Space O(NM) if __name__ == "__main__": N, M = map(int, input().split()) maze = [] for _ in range(N): maze.append(list(map(int, input()))) q = deque([(0, 0, 1)]) run = True while q and run: row, col, move = q.popleft() drow = [0, 0, -1, 1] dcol = [1, -1, 0, 0] for i in range(4): nrow, ncol = row + drow[i], col + dcol[i] if nrow >= 0 and nrow < N and ncol >= 0 and ncol < M: if nrow == N - 1 and ncol == M - 1: print(move + 1) run = False break if maze[nrow][ncol] == 0: continue if maze[nrow][ncol] == 1: q.append((nrow, ncol, move + 1)) maze[nrow][ncol] = -1

24.692308

65

0.450675

132

963

3.219697

0.431818

0.094118

0.084706

0.065882

0

0.101222

0.404984

963

38

66

25.342105

0.640489

0.125649

0

0.009639

0

1

0

false

0

0.043478

0

0.043478

0

null

0

null

0

1

0

faef5a2fc458ea2788ff3ffcc456892ea4c595bf

2,014

py

Python

VISinger/vsinging_edit.py

ishine/VITS_Singing

0343074855d049a5ab1ac4b48d436c6cc623d552

[ "Apache-2.0" ]

10

2022-03-17T03:46:13.000Z

2022-03-29T16:53:24.000Z

VISinger/vsinging_edit.py

ishine/VITS_Singing

0343074855d049a5ab1ac4b48d436c6cc623d552

[ "Apache-2.0" ]

1

2022-03-18T09:28:58.000Z

VISinger/vsinging_edit.py

ishine/VITS_Singing

0343074855d049a5ab1ac4b48d436c6cc623d552

[ "Apache-2.0" ]

3

2022-03-17T03:46:15.000Z

2022-03-18T13:55:14.000Z

import os import sys import numpy as np from scipy.io import wavfile from time import * import torch import utils from models import SynthesizerTrn def save_wav(wav, path, rate): wav *= 32767 / max(0.01, np.max(np.abs(wav))) * 0.6 wavfile.write(path, rate, wav.astype(np.int16)) # define model and load checkpoint hps = utils.get_hparams_from_file("./configs/singing_base.json") net_g = SynthesizerTrn( hps.data.filter_length // 2 + 1, hps.train.segment_size // hps.data.hop_length, **hps.model).cuda() _ = utils.load_checkpoint("./logs/singing_base/G_140000.pth", net_g, None) net_g.eval() # net_g.remove_weight_norm() idx = "2044001628" text_norm = np.load(f"midis/singing_label.npy") text_tone = np.load(f"midis/singing_pitch.npy") input_ids = torch.LongTensor(text_norm) tune_ids = torch.LongTensor(text_tone) input_f0 = torch.load(f"../VISinger_data/wav_dump_16k/{idx}_bits16.f0.pt") len_text = input_ids.size()[0] len_tone = tune_ids.size()[0] len_spec = input_f0.size()[-1] assert len_text == len_tone if (len_text != len_spec): len_min = min(len_text, len_spec) input_ids = input_ids[:len_min] tune_ids = tune_ids[:len_min] input_f0 = input_f0[:len_min] begin_time = time() with torch.no_grad(): x_tst = input_ids.cuda().unsqueeze(0) x_tst_lengths = torch.LongTensor([input_ids.size(0)]).cuda() t_tst = tune_ids.cuda().unsqueeze(0) t_tst_lengths = torch.LongTensor([tune_ids.size(0)]).cuda() f0_tst = input_f0.cuda().unsqueeze(0) audio = net_g.infer(x_tst, x_tst_lengths, t_tst, t_tst_lengths, f0_tst, t_tst_lengths, noise_scale=0, noise_scale_w=0, length_scale=1)[0][0,0].data.cpu().float().numpy() end_time = time() run_time = end_time - begin_time print('Syth Time (Seconds):', run_time) data_len = len(audio) / 16000 print('Wave Time (Seconds):', data_len) print('Real time Rate (%):', run_time/data_len) save_wav(audio, f"./midis/singing_edit.wav", hps.data.sampling_rate)

33.566667

174

0.701589

334

2,014

3.952096

0.335329

0.036364

0.024242

0.018182

0.028788

0

0.035673

0.150943

2,014

59

175

34.135593

0.736257

0.029295

0

0.129952

0.093502

0

0.020833

1

0.020833

false

0

0.166667

0

0.1875

0.0625

0

null

0

null

0

1

0

faef5dce83ddbe787319a52370f717747e1b49e1

2,891

py

Python

dao/mysql/localhost/pool.py

17621192638/flaskfd

e73ea0c98bb37ac92ff28b91d1e92d0d7edff41d

[ "MIT" ]

1

2020-11-03T02:05:15.000Z

dao/mysql/localhost/pool.py

17621192638/flaskfd

e73ea0c98bb37ac92ff28b91d1e92d0d7edff41d

[ "MIT" ]

null

dao/mysql/localhost/pool.py

17621192638/flaskfd

e73ea0c98bb37ac92ff28b91d1e92d0d7edff41d

[ "MIT" ]

1

2022-02-27T12:28:53.000Z

import utils.mysql.mysql_common_util as mysql_util import configparser,os,time cf = configparser.ConfigParser() cf.read(os.path.dirname(__file__)+"/../conf.ini") # conf对应的数据库key key = "localhost" # 创建当前数据库的连接池对象 class service(object): def __init__(self): environment = cf.get(key,"environment") for i in range(3): print("当前mysql运行环境: {} !!!!".format(environment)) time.sleep(0.1) self.pool = mysql_util.get_mysql_pool( host=cf.get(key,"host"), port=cf.get(key,"port"), user=cf.get(key,"user"), password=cf.get(key,"passwd"), database=cf.get(key,"db"), charset="utf8mb4" ) self.conn, self.cursor = mysql_util.get_db_from_pool(pool=self.pool) s = service() run_sql = mysql_util.get_wrapper(s.pool) run_sql_v2 = mysql_util.get_wrapper_v2(s.pool) from utils.mysql.common_dao import common_dao as common # 给公共方法加上带pool的注释器，无法通过init方法传递注释器 class common_dao(common): def __init__(self,table_name): super().__init__(table_name=table_name) escape_none_keys = ["status","email","phone","text","sentiment","img_url","dms_name","twords"] def move_none_keys(self,**kwargs): """移除不接受None的key""" model = kwargs.get("model",None) if model: escape_keys = [k for k,v in model.items() if (v ==None or v=="") and k in self.escape_none_keys] for k in escape_keys: del model[k] @mysql_util.pymysql_time_deal @run_sql(fetch_type="all") def select(self,*args, **kwargs): self.move_none_keys(**kwargs) return super().select(*args, **kwargs) @mysql_util.pymysql_time_deal @run_sql(fetch_type="one") def select_one(self,*args, **kwargs): self.move_none_keys(**kwargs) return super().select_one(*args, **kwargs) @run_sql(fetch_type=None) def update_by_id(self,*args, **kwargs): self.move_none_keys(**kwargs) return super().update_by_id(*args, **kwargs) @run_sql_v2(fetch_type=None) def update_by_id_v2(self,*args, **kwargs): self.move_none_keys(**kwargs) return super().update_by_id_v2(*args, **kwargs) @run_sql(fetch_type=None) def insert(self,*args, **kwargs): return super().insert(*args, **kwargs) @run_sql(fetch_type="one") def select_total(self,*args, **kwargs): self.move_none_keys(**kwargs) return super().select_total(*args, **kwargs) @run_sql(fetch_type=None) def delete_by_id(self,*args, **kwargs): return super().delete_by_id(*args, **kwargs) @run_sql(fetch_type=None) def delete(self,*args, **kwargs): return super().delete(*args, **kwargs) @run_sql_v2(fetch_type="all") def insert_v2(self,*args, **kwargs): return super().insert_v2(*args, **kwargs) if __name__ == '__main__': pass

30.755319

108

0.638533

398

2,891

4.351759

0.258794

0.103926

0.072748

0.060624

0.434758

0.432448

0.352771

0.283487

0.246536

0.157621

0

0.005674

0.207541

2,891

93

109

31.086022

0.750327

0.025943

0

0.188406

0

0.054843

0

1

0.173913

false

0.028986

0.043478

0.057971

0.391304

0.014493

0

null

0

null

0

1

0

faefef229f2918a8bcb2cd4cb755f12c13ba9263

2,967

py

Python

code/at_offer/finding_sorting/coding_interview39.py

zhangrong1722/interview

187a485de0774561eb843d8ee640236adda97b90

[ "Apache-2.0" ]

2

2020-01-05T07:46:20.000Z

2020-04-17T02:58:13.000Z

code/at_offer/finding_sorting/coding_interview39.py

zhangrong1722/interview

187a485de0774561eb843d8ee640236adda97b90

[ "Apache-2.0" ]

1

2020-01-05T07:50:26.000Z

2020-04-28T03:50:08.000Z

code/at_offer/finding_sorting/coding_interview39.py

zhangrong1722/interview

187a485de0774561eb843d8ee640236adda97b90

[ "Apache-2.0" ]

1

2020-04-18T03:58:26.000Z

""" 题目：数组中出现次数超过一半的数字数组中有一个数字出现的次数超过数组长度的一半，请找出这个数字。例如输入一个长度为9的数组{1,2,3,2,2,2,5,4,2}。由于数字2在数组中出现了5次，超过数组长度的一半，因此输出2。如果不存在则输出0。思路一：可遍历整个数组并且用字典记录每个元素出现的频率然后再次遍历字典如果出现频率超过一半返回该元素否则返回0 这是一种用空间换时间的做法时间复杂度为O(n) 空间复杂度为O(n) 思路二：如果某个元素出现的频率超过长度的元素则排序之后该元素必然出现在中间位置在此我们借用Partition函数每次运行该函数便能排好一个元素如果该元素位置在中间位置则为所找思路三：如果一个元素出现的次数超过了长度的一半则该元素出现的频率大于其他所有元素出现的次数之和我们用一个key表示元素 times表示对应出现的频率遍历整个数组如果出现的元素不是key 则times减1 如果是该元素则times加1 如果times为0 则换一个元素并重置times """ class Solution: def MoreThanHalfNum_Solution1(self, numbers): if len(numbers) == 0: return 0 freq = dict() for e in numbers: if e not in freq.keys(): freq[e] = 1 else: freq[e] += 1 for key, value in freq.items(): if value > len(numbers)//2: return key return 0 def MoreThanHalfNum_Solution2(self, numbers): if len(numbers) == 0: return 0 start, end = 0, len(numbers) - 1 index = self.Partition(numbers, start, end) while index != len(numbers) // 2: if index > len(numbers) // 2: index = self.Partition(numbers, start, index - 1) else: index = self.Partition(numbers, index + 1, end) if self.CheckValid(numbers, numbers[index]): return numbers[index] else: return 0 def MoreThanHalfNum_Solution3(self, numbers): if len(numbers) == 0: return 0 results, times = numbers[0], 1 for i in range(1, len(numbers)): if times == 0: results, times = numbers[i], 1 elif results == numbers[i]: times += 1 else: times -= 1 if self.CheckValid(numbers, results): return results else: return 0 def Partition(self, numbers, start, end): key = numbers[start] while start < end: while start < end and key <= numbers[end]: end -= 1 if key > numbers[end]: numbers[end], numbers[start] = numbers[start], numbers[end] while start < end and key >= numbers[start]: start += 1 if key < numbers[start]: numbers[end], numbers[start] = numbers[start], numbers[end] return start def CheckValid(self, numbers, element): times = 0 for e in numbers: if e == element: times += 1 return times > len(numbers) // 2 s = Solution() print(s.MoreThanHalfNum_Solution1([1, 2, 3, 2, 2, 2, 5, 4, 2])) print(s.MoreThanHalfNum_Solution2([1, 2, 3, 2, 2, 2, 5, 4, 2])) print(s.MoreThanHalfNum_Solution2([1, 2, 3, 6, 7, 9, 5, 4, 2])) print(s.MoreThanHalfNum_Solution3([1, 2, 3, 2, 2, 2, 5, 4, 2])) print(s.MoreThanHalfNum_Solution3([1, 2, 3, 6, 7, 9, 5, 4, 2]))

33.715909

77

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353

2,967

4.665722

0.252125

0.07286

0.010929

0.009715

0.304797

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0.248937

0.215543

0.105647

0.094718

0

0.051062

0.333333

2,967

88

78

33.715909

0.781598

0.166835

0

0.298507

0

1

0.074627

false

0

0.253731

0.074627

0

null

0

null

0

1

0

faf069762fe4a3817284e4a54927ba09ab79e1ed

9,559

py

Python

src/Processing/pipeline.py

hobbitsyfeet/3DMeasure

829dbc4e9a1974064ed7baa221c765c3c9123834

[ "MIT" ]

6

2020-01-14T14:37:31.000Z

2021-12-16T19:45:29.000Z

src/Processing/pipeline.py

hobbitsyfeet/3DMeasure

829dbc4e9a1974064ed7baa221c765c3c9123834

[ "MIT" ]

null

src/Processing/pipeline.py

hobbitsyfeet/3DMeasure

829dbc4e9a1974064ed7baa221c765c3c9123834

[ "MIT" ]

null

import pcl import open3d as o3d import numpy as np import load import plane_segmentation import eulcidian_cluster import manual_registration import filter_outliers import measure_cloud import global_registration import reg_grow_segmentation import convert import resample import voxel_grid import time import uuid from sklearn.decomposition import PCA import pandas as pd import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3D from copy import deepcopy if __name__ =="__main__": pre_clustered = False start_time = "" save_path = "F:/Data/Pipeline/" + start_time cloud_paths = load.get_files() registered_cloud = None manual_reg = False cloud_list = [] #pre-load all files for processing for path in cloud_paths: #cloud_list.append(pcl.load(path,format="ply")) cloud_list.append(o3d.io.read_point_cloud(path)) #target_cloud = pcl.load(cloud_paths[0],format="ply") target_cloud = o3d.io.read_point_cloud(path) #process each cloud counter = 0 for point_cloud in cloud_list: print(point_cloud) #point_cloud = convert.pcl_to_o3d(point_cloud) if pre_clustered == False: eulcidian_cluster.cluster_and_select(point_cloud,point_cloud) #print("Eliminating horizontal planes...") #point_cloud = plane_segmentation.segment(point_cloud) #print("Clustering Clouds...") #point_cloud = eulcidian_cluster.cluster_and_select(point_cloud, point_cloud) # point_cloud = convert.pcl_to_o3d(point_cloud) # registered_cloud = manual_registration.register(cloud_cluster_1,cloud_cluster_2) if counter > 0: # NOTE automatic registration relies on downsizing for accurate results. print("Preparing clusters for registration") #point_cloud_current = convert.pcl_to_o3d(cloud_list[counter]) #point_cloud_previous = convert.pcl_to_o3d(target_cloud) point_cloud_current = cloud_list[counter] point_cloud_previous = target_cloud # measure_cloud.manual_measure(point_cloud_current) # measure_cloud.manual_measure(point_cloud_previous) if manual_reg == False: voxel_size = 0.01 #5cm average source, target, source_down, target_down, source_fpfh, target_fpfh = \ global_registration.prepare_dataset(point_cloud_current, point_cloud_previous,voxel_size) print("Calculating Global Registration...") globally_registered_cloud = global_registration.execute_global_registration(source_down,target_down, source_fpfh,target_fpfh, voxel_size) print("Calculating ICP Registration...") result_icp = global_registration.refine_registration(source, target, source_fpfh, target_fpfh, globally_registered_cloud.transformation, voxel_size) print("Transforming Source...") print(result_icp.transformation) else: result_icp = manual_registration.register(point_cloud_current,point_cloud_previous) point_cloud_current.transform(result_icp.transformation) registered_cloud = point_cloud_current + point_cloud_previous print(registered_cloud) print(point_cloud_current) print(point_cloud_previous) # o3d.io.write_point_cloud((save_path + "registered_cloud.ply"),registered_cloud, write_ascii=True, compressed=True) # if len(registered_cloud.points) > 100: o3d.io.write_point_cloud((save_path + "regeristered_prefilter_cloud.ply"),registered_cloud,write_ascii=True) target_cloud = o3d.io.read_point_cloud((save_path + "regeristered_prefilter_cloud.ply"),format="ply") registered_cloud = convert.o3d_to_pcl(registered_cloud) # registered_cloud = convert.o3d_to_pcl(registered_cloud) # pcl.save(registered_cloud,(save_path + "regeristered_prefilter_cloud.ply"), format="ply") # target_cloud = pcl.load(save_path + "regeristered_prefilter_cloud.ply") #remove outliers registered_cloud = filter_outliers.statistical_filter(registered_cloud) #smooth data registered_cloud = resample.smooth(registered_cloud,0.01) # average and reduce point size registered_cloud = voxel_grid.filter(registered_cloud,leaf_size=0.01) #0.1cm # target_cloud = deepcopy(point_cloud_current + point_cloud_previous) print("Saving...") pcl.save(registered_cloud,(save_path + "registered_cloud.ply"), format="ply") print("Measuring") measure_cloud.manual_measure(convert.pcl_to_o3d(registered_cloud)) ''' smoothness = "" PCA_results = [] #Perform PCA on entire object df = pd.DataFrame(convert.pcl_to_numpy(registered_cloud)) pca = PCA(n_components=3) pca.fit(df) # Store results of PCA in a data frame result = pd.DataFrame(pca.transform(df), columns=['PCA%i' % i for i in range(3)], index=df.index) print (result) PCA_results.append(deepcopy(result)) #perform PCA on each cluster while(str(smoothness) is not ""): clusters = reg_grow_segmentation.segment(registered_cloud, float(smoothness),min_cluster=5, view=True) for cluster in clusters: print("Starting Statistical Analysis...") df = pd.DataFrame(convert.pcl_to_numpy(cluster)) pca = PCA(n_components=3) pca.fit(df) # Store results of PCA in a data frame result = pd.DataFrame(pca.transform(df), columns=['PCA%i' % i for i in range(3)], index=df.index) PCA_results.append(deepcopy(result)) print (result) smoothness = input() show_fig_flag = 0 while show_fig_flag is not -1: # Plot initialisation fig = plt.figure() ax = fig.add_subplot(111, projection='3d') ax.scatter(PCA_results[show_fig_flag]['PCA0'], PCA_results[show_fig_flag]['PCA1'], PCA_results[show_fig_flag]['PCA2'], cmap="Set2_r", s=60) # make simple, bare axis lines through space: xAxisLine = ((min(PCA_results[show_fig_flag]['PCA0']), max(PCA_results[show_fig_flag]['PCA0'])), (0, 0), (0,0)) ax.plot(xAxisLine[0], xAxisLine[1], xAxisLine[2], 'r') yAxisLine = ((0, 0), (min(PCA_results[show_fig_flag]['PCA1']), max(PCA_results[show_fig_flag]['PCA1'])), (0,0)) ax.plot(yAxisLine[0], yAxisLine[1], yAxisLine[2], 'r') zAxisLine = ((0, 0), (0,0), (min(PCA_results[show_fig_flag]['PCA2']), max(PCA_results[show_fig_flag]['PCA2']))) ax.plot(zAxisLine[0], zAxisLine[1], zAxisLine[2], 'r') # label the axes ax.set_xlabel("X") ax.set_ylabel("Y") ax.set_zlabel("Z") ax.set_title("") plt.show(block=False) print("Which cluster would you like to see? Enter -1 to continue") show_fig_flag = int(input()) #convert to o3d to measure # registered_cloud = convert.pcl_to_o3d(registered_cloud) print("Starting Statistical Analysis...") df = pd.DataFrame(convert.pcl_to_numpy(registered_cloud)) pca = PCA(n_components=3) pca.fit(df) # Store results of PCA in a data frame result = pd.DataFrame(pca.transform(df), columns=['PCA%i' % i for i in range(3)], index=df.index) print (result) # Plot initialisation fig = plt.figure() ax = fig.add_subplot(111, projection='3d') ax.scatter(result['PCA0'], result['PCA1'], result['PCA2'], cmap="Set2_r", s=60) # make simple, bare axis lines through space: xAxisLine = ((min(result['PCA0']), max(result['PCA0'])), (0, 0), (0,0)) ax.plot(xAxisLine[0], xAxisLine[1], xAxisLine[2], 'r') yAxisLine = ((0, 0), (min(result['PCA1']), max(result['PCA1'])), (0,0)) ax.plot(yAxisLine[0], yAxisLine[1], yAxisLine[2], 'r') zAxisLine = ((0, 0), (0,0), (min(result['PCA2']), max(result['PCA2']))) ax.plot(zAxisLine[0], zAxisLine[1], zAxisLine[2], 'r') # label the axes ax.set_xlabel("Width") ax.set_ylabel("Height") ax.set_zlabel("Depth") ax.set_title("") plt.show(block=False) ''' counter += 1

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155

0.57642

1,064

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4.924812

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0.066794

0.025191

0.029198

0.512595

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0.148649

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null

0

null

0

1

0

faf21fdb1a2f9c963f6c5f900fde995eea67ab14

14,673

py

Python

pymoldflow/studyrlt.py

tianyikillua/pymoldflow

93a0d4a1ee6dcd07584d8bf4227f618f5333f05b

[ "MIT" ]

1

2020-11-22T16:04:48.000Z

pymoldflow/studyrlt.py

tianyikillua/pymoldflow

93a0d4a1ee6dcd07584d8bf4227f618f5333f05b

[ "MIT" ]

null

pymoldflow/studyrlt.py

tianyikillua/pymoldflow

93a0d4a1ee6dcd07584d8bf4227f618f5333f05b

[ "MIT" ]

null

import os import shutil import subprocess import numpy as np import meshio from .base import MoldflowAutomation from .data_io import PatranMesh, convert_to_time_series_xdmf, read_moldflow_xml class MoldflowResultsExporter(MoldflowAutomation): """ Export Autodesk Moldflow simulation results Args: moldflow_path (str): Path to Autodesk Moldflow Insight sdyfile (str): Autodesk Moldflow ``.sdy`` file containing simulation results outdir (str): Output directory outfile (str): Output file with a format compatible with `meshio <https://github.com/nschloe/meshio>`_ use_metric_units (bool): Use Metric units (mm for length for instance) verbose (bool): Print out progress information stdout (obj): Redirect progress information """ def __init__( self, moldflow_path, sdyfile=None, outdir=None, outfile=None, use_metric_units=True, verbose=True, stdout=None, ): super().__init__(moldflow_path, use_metric_units, verbose, stdout) self.sdyfile = sdyfile self.outdir = outdir self.outfile = outfile self.mesh = None def check(self): """ Check if the provided ``studyrlt.exe`` program works """ self._print("Checking that studyrlt works fine...") success, _ = self._run_studyrlt(None) return success def export_log(self): """ Export analysis log to ``log.txt`` Returns: bool: Success indicator """ log = os.path.join(self._export_dir(), "log.txt") # Run studyrlt self._print("Exporting log file...") success, log_ = self._run_studyrlt("exportoutput") if success: shutil.move(log_, log) return success def export_mesh( self, output_formats=[], only_export_rawdata=False, return_mesh=False ): """ Export and optionally process mesh information Args: output_formats (list): List of `meshio <https://github.com/nschloe/meshio>`_-compatible mesh formats (MED, XDMF, ...) to be exported only_export_rawdata (bool): Whether only export the raw ``.pat`` Patran mesh without processing return_mesh (bool): Whether also return the ``meshio`` mesh object Returns: bool: Success indicator """ mesh = os.path.join(self._rawdata_dir(), "mesh.pat") # Run studyrlt if mesh doesn't exist if not os.path.isfile(mesh): self._print("Mesh: running studyrlt...") success, mesh_ = self._run_studyrlt("exportpatran") if success: shutil.move(mesh_, mesh) else: return False else: self._print("Mesh: Patran file already generated") if only_export_rawdata: return True # Read and process mesh self._print("Mesh: reading Patran file...") self.mesh = PatranMesh(mesh, read_celltypes=["triangle", "tetra"]) if self.use_metric_units: self.mesh.scale() # convert to mm # Only keep one cell type (2d triangular or 3d tetra) if "tetra" in self.mesh.cells: self.mesh.cell_type = "tetra" elif "triangle" in self.mesh.cells: self.mesh.cell_type = "triangle" self.mesh.cells = {self.mesh.cell_type: self.mesh.cells[self.mesh.cell_type]} self.mesh.cellsID = self.mesh.cellsID[self.mesh.cell_type] self.mesh.cellsID = dict( zip(self.mesh.cellsID, np.arange(len(self.mesh.cellsID))) ) self.mesh.point_data = {} self.mesh.cell_data[self.mesh.cell_type] = {} # Export to specified formats def _output_mesh(ext): os.makedirs(self._interfaces_dir(), exist_ok=True) out = os.path.join(self._interfaces_dir(), "mesh." + ext) meshio.write(out, self.mesh) for ext in output_formats: _output_mesh(ext) if return_mesh: return True, self.mesh else: return True def export_result( self, resultID, name, only_last_step=True, export_npy=False, only_export_rawdata=False, return_array=False, ): """ Export and optionally process simulation results Args: resultID (int): Identifier of the simulation result (refer to ``results.dat``) name (str): Name of the provided simulation result only_last_step (bool): Only process the last time-step export_npy (bool): Whether also export raw numerical values only_export_rawdata (bool): Whether only export the raw ``.xml`` file without processing return_array (bool): Whether also return the ``numpy`` array for fields defined at a single time-step Returns: int: Success indicator, (1) success; (-1) run_studyrlt error; (-2) read_moldflow_xml error """ xml = os.path.join(self._rawdata_dir(), "{}.xml".format(self._io_name(name))) # Run studyrlt if xml doesn't exist if not os.path.isfile(xml): self._print("{}: running studyrlt...".format(name)) success, xml_ = self._run_studyrlt(resultID) if success: shutil.move(xml_, xml) else: if return_array: return -1, None else: return -1 else: self._print("{}: XML file already generated".format(name)) if only_export_rawdata: return 1 self._print("{}: parsing XML...".format(name)) success, data = read_moldflow_xml(xml, only_last_step=only_last_step) if not success: if return_array: return -2, None else: return -2 # Process and export data if data["type"] == "NMDT(Non-mesh data)": array = self._process_nmdt_result(data, name, return_array=return_array) elif data["time"] is None: array = self._process_single_result( data, name, export_npy=export_npy, return_array=return_array ) else: self._process_time_series_result(data, name) if return_array: return 1, array else: return 1 def finalize(self): """ Post-process the output file Currently it will generate a time-series XDMF file """ # Convert to a time-series XDMF file if os.path.isfile(self.outfile) and ".xdmf" in self.outfile: convert_to_time_series_xdmf(self.outfile, backup=False) def _export_dir(self): if self.outdir is None and self.outfile is not None: return os.path.dirname(self.outfile) elif self.outdir is None: return os.path.dirname(self.sdyfile) else: return self.outdir def _rawdata_dir(self): return os.path.join(self._export_dir(), "rawdata") def _interfaces_dir(self): return os.path.join(self._export_dir(), "interfaces") def _io_name(self, name): return name.lower().replace(" ", "_").replace("/", "").replace(",", "") def _run_studyrlt(self, action): sdy = self.sdyfile check_mode = False # flag to verify if studyrlt works fine command = [self.studyrlt_exe, self.sdyfile] if action == "exportpatran": command.append("-exportpatran") out_ = sdy.replace(".sdy", ".pat") elif action == "exportoutput": command.append("-exportoutput") out_ = sdy.replace(".sdy", ".txt") elif type(action) == int: command.append("-xml") command.append("{:d}".format(action)) out_ = sdy.replace(".sdy", ".xml") else: check_mode = True if not check_mode and self.use_metric_units: command.append("-unit") command.append("Metric") if not check_mode: assert os.path.isfile(sdy) # Execute the command, if there is an execution error, then we # have a problem with studyrlt try: CREATE_NO_WINDOW = 0x08000000 proc = subprocess.Popen( command, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, creationflags=CREATE_NO_WINDOW, ) output, _ = proc.communicate() proc.wait() except subprocess.SubprocessError: print("Unable to run {}".format(" ".join(command))) return False, None # If the output does not contain Autodesk, then problem with studyrlt output = output.decode("windows-1252").strip() if "Autodesk" not in output: print("Verify that the given studyrlt.exe works") return False, None # Directly return for check mode, since no output is expected if check_mode: return True, None # Cleanups tmps = [sdy.replace(".sdy", ".out"), sdy.replace(".sdy", ".err")] for tmp in tmps: if os.path.isfile(tmp): os.remove(tmp) # If can not find the output file, then we have a problem with studyrlt if os.path.isfile(out_): os.makedirs(self._rawdata_dir(), exist_ok=True) out = os.path.join(self._rawdata_dir(), os.path.basename(out_)) shutil.move(out_, out) return True, out else: print("Unable to retrieve outputs for {}".format(" ".join(command))) return False, None def _prepare_data_structure(self, data): if data["type"] == "NDDT(Node data)": num = len(self.mesh.points) locate = self.mesh.pointsID else: num = len(self.mesh.cells[self.mesh.cell_type]) locate = self.mesh.cellsID if data["dim"] == 1: values = np.full(num, np.nan) else: values = np.full((num, data["dim"]), np.nan) return locate, values def _process_nmdt_result(self, data, name, return_array=False): # TODO: not necessarily time in fact # TODO: multidimensional values? import xlsxwriter x = data["time"] y = data["val"] assert len(x) == len(y) length = len(x) if self.outfile is not None: # Open an Excel file out = os.path.join(self._export_dir(), self._io_name(name) + ".xlsx") workbook = xlsxwriter.Workbook(out) worksheet = workbook.add_worksheet() bold = workbook.add_format({"bold": 1}) # Dump data name = f"{name} ({data['unit']})" worksheet.write_row("A1", ["Time (s)", name], bold) worksheet.write_column("A2", x) worksheet.write_column("B2", y) # Plot chart chart = workbook.add_chart({"type": "scatter", "subtype": "straight"}) chart.add_series( { "name": ["Sheet1", 0, 1], "categories": ["Sheet1", 1, 0, length, 0], "values": ["Sheet1", 1, 1, length, 1], } ) chart.set_x_axis({"name": "Time (s)", "major_gridlines": {"visible": True}}) chart.set_y_axis({"name": name}) chart.set_size({"x_scale": 2, "y_scale": 2}) chart.set_legend({"none": True}) worksheet.insert_chart("E2", chart) workbook.close() if return_array: return x, y def _process_single_result(self, data, name, export_npy=False, return_array=False): # Prepare data structure locate, values = self._prepare_data_structure(data) # Read data val = data["val"] for identifier, value in val.items(): try: values[locate[identifier]] = value except Exception: pass # For 6-dimensional values, reverse 13 and 23 if data["dim"] == 6: values = values[:, [0, 1, 2, 3, 5, 4]] # Export to the output file if self.outfile is not None: if data["type"] == "NDDT(Node data)": self.mesh.point_data[name] = values else: self.mesh.cell_data[self.mesh.cell_type][name] = values meshio.write(self.outfile, self.mesh) # Export raw values if export_npy: os.makedirs(self._interfaces_dir(), exist_ok=True) out = os.path.join(self._interfaces_dir(), f"{self._io_name(name)}.npy") np.save(out, values) # Export array if return_array: return values else: return None def _process_time_series_result(self, data, name): # Prepare PVD information timestep = data["time"] nsteps = len(timestep) name_step = [f"{name}__{t:.4f}" for t in timestep] # Read each time-step locate, values_ = self._prepare_data_structure(data) for i in range(nsteps): self._print(f"{name}: reading time-step #{i + 1:d}/{nsteps:d}...") # Read data values = np.copy(values_) for identifier, value in data["val"][i].items(): try: values[locate[identifier]] = value except Exception: pass # For 6-dimensional values, reverse 13 and 23 if data["dim"] == 6: values = values[:, [0, 1, 2, 3, 5, 4]] # Save to the mesh data structure if self.outfile is not None: if data["type"] == "NDDT(Node data)": self.mesh.point_data[name_step[i]] = values else: self.mesh.cell_data[self.mesh.cell_type][name_step[i]] = values # Final write if self.outfile is not None: meshio.write(self.outfile, self.mesh)

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0.188214

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14,673

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false

0.007299

0.029197

0.010949

0.193431

0.043796

0

null

0

null

0

1

0

faf5650e8a3f52def370becc66cede60c369028e

2,738

py

Python

test/integ_tests/test_integration.py

antalszava/amazon-braket-pennylane-plugin-python-1

0228fd38dee5a586807b8a2b32b3bfa0f0360669

[ "Apache-2.0" ]

16

2021-01-11T20:59:39.000Z

2022-03-04T14:18:20.000Z

test/integ_tests/test_integration.py

antalszava/amazon-braket-pennylane-plugin-python-1

0228fd38dee5a586807b8a2b32b3bfa0f0360669

[ "Apache-2.0" ]

43

2020-12-09T00:19:38.000Z

2022-03-29T19:52:55.000Z

test/integ_tests/test_integration.py

aws/amazon-braket-pennylane-plugin-python

dbb8f4ae6d82778a9efb0a7f635100be6e323024

[ "Apache-2.0" ]

11

2021-01-11T21:01:42.000Z

2021-11-01T08:46:11.000Z

# Copyright Amazon.com Inc. or its affiliates. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"). You # may not use this file except in compliance with the License. A copy of # the License is located at # # http://aws.amazon.com/apache2.0/ # # or in the "license" file accompanying this file. This file is # distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF # ANY KIND, either express or implied. See the License for the specific # language governing permissions and limitations under the License. """Tests that plugin devices are accessible and integrate with PennyLane""" import numpy as np import pennylane as qml import pkg_resources import pytest from conftest import shortname_and_backends ENTRY_POINTS = {entry.name: entry for entry in pkg_resources.iter_entry_points("pennylane.plugins")} class TestDeviceIntegration: """Test the devices work correctly from the PennyLane frontend.""" @pytest.mark.parametrize("d", shortname_and_backends) def test_load_device(self, d, extra_kwargs): """Test that the device loads correctly""" dev = TestDeviceIntegration._device(d, 2, extra_kwargs) assert dev.num_wires == 2 assert dev.shots is None assert dev.short_name == d[0] def test_args_aws(self): """Test that BraketAwsDevice requires correct arguments""" with pytest.raises(TypeError, match="missing 3 required positional arguments"): qml.device("braket.aws.qubit") def test_args_local(self): """Test that BraketLocalDevice requires correct arguments""" with pytest.raises(TypeError, match="missing 1 required positional argument"): qml.device("braket.local.qubit") @pytest.mark.parametrize("d", shortname_and_backends) @pytest.mark.parametrize("shots", [None, 8192]) def test_one_qubit_circuit(self, shots, d, tol, extra_kwargs): """Test that devices provide correct result for a simple circuit""" dev = TestDeviceIntegration._device(d, 1, extra_kwargs) a = 0.543 b = 0.123 c = 0.987 @qml.qnode(dev) def circuit(x, y, z): """Reference QNode""" qml.BasisState(np.array([1]), wires=0) qml.Hadamard(wires=0) qml.Rot(x, y, z, wires=0) return qml.expval(qml.PauliZ(0)) assert np.allclose(circuit(a, b, c), np.cos(a) * np.sin(b), **tol) @staticmethod def _device(shortname_and_backend, wires, extra_kwargs): device_name, backend = shortname_and_backend device_class = ENTRY_POINTS[device_name].load() return qml.device(device_name, wires=wires, **extra_kwargs(device_class, backend))

38.56338

100

0.688459

371

2,738

4.97035

0.412399

0.032538

0.023861

0.111714

0.066161

0

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2,738

70

101

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0

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0

0.105263

1

0.157895

false

0

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0

0.368421

0

null

0

null

0

1

0

faf7987b61f1f17233d706e7eace5e40be0ca94e

792

py

Python

HMIN320/Stereovision/stereovision.py

Eikins/M2-Imagina

6e37ef755bd5312fc808ec599b3bd76084d35568

[ "MIT" ]

null

HMIN320/Stereovision/stereovision.py

Eikins/M2-Imagina

6e37ef755bd5312fc808ec599b3bd76084d35568

[ "MIT" ]

null

HMIN320/Stereovision/stereovision.py

Eikins/M2-Imagina

6e37ef755bd5312fc808ec599b3bd76084d35568

[ "MIT" ]

null

from enum import Enum import cv2 class Camera(Enum): LEFT = 0, RIGHT = 1 def OnClick(event, x, y, flags, params): if event == cv2.EVENT_LBUTTONDOWN: if (params == Camera.LEFT): print ("DDD") elif (params == Camera.RIGHT): print ("MDR") leftImage = cv2.imread("images/TurtleG.tif") rightImage = cv2.imread("images/TurtleD.tif") cv2.namedWindow("Left Image", cv2.WINDOW_NORMAL) cv2.namedWindow("Right Image", cv2.WINDOW_NORMAL) cv2.setMouseCallback("Left Image", OnClick, Camera.LEFT) # Left cv2.setMouseCallback("Right Image", OnClick, Camera.RIGHT) # Right cv2.imshow("Left Image", leftImage) cv2.imshow("Right Image", rightImage) while True: key = cv2.waitKey(0) if key == 27: cv2.destroyAllWindows() break

24

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0.660354

101

792

5.148515

0.425743

0.051923

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null

0

null

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1

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fafa000b315bfac007911e703fb6b8b09f2b8a9d

6,162

py

Python

stackexchange/web.py

lizmat/soiqbot

eb1c922418a7fc58bdbbb3573c48f90cdd155667

[ "MIT" ]

9

2016-03-24T19:47:53.000Z

2022-01-13T19:07:07.000Z

stackexchange/web.py

lizmat/soiqbot

eb1c922418a7fc58bdbbb3573c48f90cdd155667

[ "MIT" ]

null

stackexchange/web.py

lizmat/soiqbot

eb1c922418a7fc58bdbbb3573c48f90cdd155667

[ "MIT" ]

4

2016-04-18T14:38:19.000Z

2020-01-14T15:42:09.000Z

# stackweb.py - Core classes for web-request stuff from __future__ import print_function from stackexchange.core import StackExchangeError from six.moves import urllib import datetime, operator, io, gzip, time import datetime try: import json except ImportError: import simplejson as json class TooManyRequestsError(Exception): def __str__(self): return "More than 30 requests have been made in the last five seconds." class WebRequest(object): data = '' info = None def __init__(self, data, info): self.data = data self.info = info def __str__(self): return str(self.data) class WebRequestManager(object): debug = False cache = {} def __init__(self, impose_throttling = False, throttle_stop = True, cache = True, cache_age = 1800): # Whether to monitor requests for overuse of the API self.impose_throttling = impose_throttling # Whether to throw an error (when True) if the limit is reached, or wait until another request # can be made (when False). self.throttle_stop = throttle_stop # Whether to use request caching. self.do_cache = cache # The time, in seconds, for which to cache a response self.cache_age = cache_age # The time at which we should resume making requests after receiving a 'backoff' for each method self.backoff_expires = {} # When we last made a request window = datetime.datetime.now() # Number of requests since last throttle window num_requests = 0 def debug_print(self, *p): if WebRequestManager.debug: print(' '.join([x if isinstance(x, str) else repr(x) for x in p])) def canon_method_name(self, url): # Take the URL relative to the domain, without initial / or parameters parsed = urllib.parse.urlparse(url) return '/'.join(parsed.path.split('/')[1:]) def request(self, url, params): now = datetime.datetime.now() # Quote URL fields (mostly for 'c#'), but not : in http:// components = url.split('/') url = components[0] + '/' + ('/'.join(urllib.parse.quote(path) for path in components[1:])) done = False for k, v in params.items(): if not done: url += '?' done = True else: url += '&' url += '%s=%s' % (k, urllib.parse.quote(str(v).encode('utf-8'))) # Now we have the `proper` URL, we can check the cache if self.do_cache and url in self.cache: timestamp, data = self.cache[url] self.debug_print('C>', url, '@', timestamp) if (now - timestamp).seconds <= self.cache_age: self.debug_print('Hit>', url) return data # Before we do the actual request, are we going to be throttled? def halt(wait_time): if self.throttle_stop: raise TooManyRequestsError() else: # Wait the required time, plus a bit of extra padding time. time.sleep(wait_time + 0.1) if self.impose_throttling: # We need to check if we've been told to back off method = self.canon_method_name(url) backoff_time = self.backoff_expires.get(method, None) if backoff_time is not None and backoff_time >= now: self.debug_print('backoff: %s until %s' % (method, backoff_time)) halt((now - backoff_time).seconds) if (now - WebRequestManager.window).seconds >= 5: WebRequestManager.window = now WebRequestManager.num_requests = 0 WebRequestManager.num_requests += 1 if WebRequestManager.num_requests > 30: halt(5 - (now - WebRequestManager.window).seconds) # We definitely do need to go out to the internet, so make the real request self.debug_print('R>', url) request = urllib.request.Request(url) request.add_header('Accept-encoding', 'gzip') req_open = urllib.request.build_opener() try: conn = req_open.open(request) info = conn.info() req_data = conn.read() error_code = 200 except urllib.error.HTTPError as e: # we'll handle the error response later error_code = e.code # a hack (headers is an undocumented property), but there's no sensible way to get them info = getattr(e, 'headers', {}) req_data = e.read() # Handle compressed responses. # (Stack Exchange's API sends its responses compressed but intermediary # proxies may send them to us decompressed.) if info.get('Content-Encoding') == 'gzip': data_stream = io.BytesIO(req_data) gzip_stream = gzip.GzipFile(fileobj = data_stream) actual_data = gzip_stream.read() else: actual_data = req_data # Check for errors if error_code != 200: try: error_ob = json.loads(actual_data.decode('utf8')) except: raise StackExchangeError() else: raise StackExchangeError(error_ob.get('error_id', StackExchangeError.UNKNOWN), error_ob.get('error_name'), error_ob.get('error_message')) conn.close() req_object = WebRequest(actual_data, info) # Let's store the response in the cache if self.do_cache: self.cache[url] = (now, req_object) self.debug_print('Store>', url) return req_object def json_request(self, to, params): req = self.request(to, params) parsed_result = json.loads(req.data.decode('utf8')) # In API v2.x we now need to respect the 'backoff' warning if 'backoff' in parsed_result: method = self.canon_method_name(to) self.backoff_expires[method] = datetime.datetime.now() + datetime.timedelta(seconds = parsed_result['backoff']) return (parsed_result, req.info)

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0

null

0

null

0

1

0

fafc0e34e7de3b181121e76478e0612c34963839

1,484

py

Python

setup.py

timgates42/try

785798471ae753968053641b6407ac7be9f91309

[ "MIT" ]

691

2016-03-21T04:44:02.000Z

2022-03-30T03:59:31.000Z

setup.py

timgates42/try

785798471ae753968053641b6407ac7be9f91309

[ "MIT" ]

16

2016-03-21T11:27:10.000Z

2021-12-01T01:51:32.000Z

setup.py

timgates42/try

785798471ae753968053641b6407ac7be9f91309

[ "MIT" ]

47

2016-03-21T05:04:56.000Z

2022-03-09T04:45:26.000Z

# -*- coding: utf-8 -*- """ Setup try package. """ import ast import re from setuptools import setup, find_packages def get_version(): """Gets the current version""" _version_re = re.compile(r"__VERSION__\s+=\s+(.*)") with open("trypackage/__init__.py", "rb") as init_file: version = str(ast.literal_eval(_version_re.search( init_file.read().decode("utf-8")).group(1))) return version setup( name="trypackage", version=get_version(), license="MIT", description="Awesome cli tool to try out python packages", author="Timo Furrer", author_email="tuxtimo@gmail.com", url="https://github.com/timofurrer/try", packages=find_packages(), include_package_data=True, install_requires=["click"], entry_points={ "console_scripts": [ "try=trypackage.__main__:main", ] }, keywords=[ "try", "python", "packages", "pypi", "github", "interactive", "console", "ipython", "versions", "virtualenv" ], classifiers=[ "Programming Language :: Python", "Programming Language :: Python :: 3", "Programming Language :: Python :: 3.4", "Programming Language :: Python :: 3.5", "Operating System :: OS Independent", "Environment :: Console", "License :: OSI Approved :: MIT License", "Intended Audience :: End Users/Desktop", "Topic :: Utilities", ], )

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0.118624

0.092527

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0.257412

1,484

63

23.555556

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0.044474

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0.022727

false

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0.113636

0

null

0

null

0

1

0

fafc3c938fdc7c34c445e7c2c07bfa0509f1663a

5,470

py

Python

mbed_host_tests/host_tests_registry/host_registry.py

screamerbg/htrun

1c570ec77d8c5673dae55dc790302d86d712c36b

[ "Apache-2.0" ]

null

mbed_host_tests/host_tests_registry/host_registry.py

screamerbg/htrun

1c570ec77d8c5673dae55dc790302d86d712c36b

[ "Apache-2.0" ]

null

mbed_host_tests/host_tests_registry/host_registry.py

screamerbg/htrun

1c570ec77d8c5673dae55dc790302d86d712c36b

[ "Apache-2.0" ]

null

""" mbed SDK Copyright (c) 2011-2015 ARM Limited Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. Author: Przemyslaw Wirkus <Przemyslaw.Wirkus@arm.com> """ try: from imp import load_source except ImportError: import importlib import sys def load_source(module_name, file_path): spec = importlib.util.spec_from_file_location(module_name, file_path) module = importlib.util.module_from_spec(spec) spec.loader.exec_module(module) sys.modules[module_name] = module return module from inspect import getmembers, isclass from os import listdir from os.path import abspath, exists, isdir, isfile, join from ..host_tests.base_host_test import BaseHostTest class HostRegistry: """ Class stores registry with host tests and objects representing them """ HOST_TESTS = {} # Map between host_test_name -> host_test_object def register_host_test(self, ht_name, ht_object): """! Registers host test object by name @param ht_name Host test unique name @param ht_object Host test class object """ if ht_name not in self.HOST_TESTS: self.HOST_TESTS[ht_name] = ht_object def unregister_host_test(self, ht_name): """! Unregisters host test object by name @param ht_name Host test unique name """ if ht_name in self.HOST_TESTS: del self.HOST_TESTS[ht_name] def get_host_test(self, ht_name): """! Fetches host test object by name @param ht_name Host test unique name @return Host test callable object or None if object is not found """ return self.HOST_TESTS[ht_name] if ht_name in self.HOST_TESTS else None def is_host_test(self, ht_name): """! Checks (by name) if host test object is registered already @param ht_name Host test unique name @return True if ht_name is registered (available), else False """ return (ht_name in self.HOST_TESTS and self.HOST_TESTS[ht_name] is not None) def table(self, verbose=False): """! Prints list of registered host test classes (by name) @Detail For devel & debug purposes """ from prettytable import PrettyTable, HEADER column_names = ['name', 'class', 'origin'] pt = PrettyTable(column_names, junction_char="|", hrules=HEADER) for column in column_names: pt.align[column] = 'l' for name, host_test in sorted(self.HOST_TESTS.items()): cls_str = str(host_test.__class__) if host_test.script_location: src_path = host_test.script_location else: src_path = 'mbed-host-tests' pt.add_row([name, cls_str, src_path]) return pt.get_string() def register_from_path(self, path, verbose=False): """ Enumerates and registers locally stored host tests Host test are derived from mbed_host_tests.BaseHostTest classes """ if path: path = path.strip('"') if verbose: print("HOST: Inspecting '%s' for local host tests..." % path) if exists(path) and isdir(path): python_modules = [ f for f in listdir(path) if isfile(join(path, f)) and f.endswith(".py") ] for module_file in python_modules: self._add_module_to_registry(path, module_file, verbose) def _add_module_to_registry(self, path, module_file, verbose): module_name = module_file[:-3] try: mod = load_source(module_name, abspath(join(path, module_file))) except Exception as e: print( "HOST: Error! While loading local host test module '%s'" % join(path, module_file) ) print("HOST: %s" % str(e)) return if verbose: print("HOST: Loading module '%s': %s" % (module_file, str(mod))) for name, obj in getmembers(mod): if ( isclass(obj) and issubclass(obj, BaseHostTest) and str(obj) != str(BaseHostTest) ): if obj.name: host_test_name = obj.name else: host_test_name = module_name host_test_cls = obj host_test_cls.script_location = join(path, module_file) if verbose: print( "HOST: Found host test implementation: %s -|> %s" % (str(obj), str(BaseHostTest)) ) print( "HOST: Registering '%s' as '%s'" % (str(host_test_cls), host_test_name) ) self.register_host_test( host_test_name, host_test_cls() )

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5,470

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null

0

null

0

1

0

fafd5c22c2e8d5d921e379d038c9cca8e51e0945

845

py

Python

proxypool/crawlers/public/ip3366.py

wp4969/ProxyPool

b51c08466f208aedd823105bf1c82bc22d890b58

[ "MIT" ]

null

proxypool/crawlers/public/ip3366.py

wp4969/ProxyPool

b51c08466f208aedd823105bf1c82bc22d890b58

[ "MIT" ]

null

proxypool/crawlers/public/ip3366.py

wp4969/ProxyPool

b51c08466f208aedd823105bf1c82bc22d890b58

[ "MIT" ]

null

from proxypool.crawlers.base import BaseCrawler PROXY_TYPE = range(1, 3) MAX_PAGE = 7 BASE_URL = 'http://www.ip3366.net/free/?stype={stype}&page={page}' class Ip3366Crawl(BaseCrawler): """ ip3366 http://www.ip3366.net 如果不想获取器执行这个代理可以设置：ignore = True """ urls = [BASE_URL.format(stype=stype, page=page) for stype in PROXY_TYPE for page in range(1, MAX_PAGE+1)] ignore = False def parse(self, response): trs = response.xpath('//div[@id="list"]/table/tbody/tr') for tr in trs: ip = tr.xpath('.//td[1]/text()')[0] port = tr.xpath('.//td[2]/text()')[0] proxy = '{}:{}'.format(ip, port) #elite = '高匿' in tr.xpath('.//td[3]/text()').get() #https = 'HTTPS' in tr.xpath('.//td[4]/text()').get() yield proxy

32.5

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false

0

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0.357143

0

null

0

null

0

1

0

fafeb0eba344c4cb37e2d3495da38b5e73a365ff

10,982

py

Python

visualization/dashapp/layouts.py

Lchuang/yews

254c1d3887b812a94421bd6ccef4a51a7ef330e0

[ "Apache-2.0" ]

null

visualization/dashapp/layouts.py

Lchuang/yews

254c1d3887b812a94421bd6ccef4a51a7ef330e0

[ "Apache-2.0" ]

null

visualization/dashapp/layouts.py

Lchuang/yews

254c1d3887b812a94421bd6ccef4a51a7ef330e0

[ "Apache-2.0" ]

null

#!/Users/lindsaychuang/miniconda3/envs/obspy/bin/python # this file defines all the layouts used in apps import dash_core_components as dcc import dash_html_components as html import dash_table import dash_daq as daq from dash_table.Format import Format import datetime # ---- 01. map layout ------ map_layout = { 'mapbox': { 'accesstoken': 'pk.eyJ1IjoieWNodWFuZzM1IiwiYSI6ImNqeGtzZDluZzFkcWgzem12ZDY2cWpoemsifQ.1_ZAhhpXtE2hnkSCtKIDZw', 'style': 'mapbox://styles/ychuang35/cjxlwvwur15fj1cousoa18kju', 'center': {'lat': 23.60, 'lon': 121.0}, 'zoom': 7, }, 'hovermode': 'closest', 'height': 800, 'yaxis': {'title': 'Latitude'}, 'xaxis': {'title': 'Longitude'}, 'margin': {"r": 0, "t": 0, "l": 5, "b": 0} } map_data = [ {'name': "events", 'marker': { 'opacity': 0.8, 'colorscale': 'Portland', 'cmax': 10, 'cmin': 0, 'colorbar': {'title': 'phase number'}, 'showscale': True, 'sizeref': 0.4 }, 'type': 'scattermapbox', 'showlegend': False, 'hovertemplate': '(%{lat:.2f}, %{lon:.2f})' '<br><b>%{text}</b>' '<br><b>M: %{marker.size:.f}</b>' '<br><b>Phase: %{marker.color:.f}</b>' }, {'name': "stations", 'marker': { 'color': 'rgb(0, 0, 0)', 'size': 9, 'opacity': 1, 'symbol': 'triangle', 'line': {'color': 'rgb(0, 0, 0)'}, }, 'type': 'scattermapbox', 'showlegend': False, 'hovertemplate': '(%{lat:.2f}, %{lon:.2f})' '<br><b>%{text}</b>' }, ] geomap_layout = dcc.Graph(id='map', figure={ 'layout': map_layout, 'data': map_data }) # ---- 02. earthquake catalog drop-down list eqs_loc_layout = dcc.Dropdown(id='eq_loc_dw', options=[], placeholder='select a standard earthquake catalog', multi=True ) # ---- 03. earthquake catalog table eqs_table_layout = dash_table.DataTable(id='table', data=[], style_header={ 'backgroundColor': 'rgb(230,230,230)', 'fontWeight': 'bold', 'textAlign': 'center' }, style_data_conditional=[{ 'if': {'row_index': 'odd'}, 'backgroundColor': 'rgb(2489,248,248)' }], style_table={ #'maxHeight': '400px', 'overflowY': 'scroll', 'overflowX': 'scroll', #'maxWidth': '200px', 'textAlign': 'center' }, columns=[ {'id': 'otime', 'name': 'otime'}, {'id': 'evla', 'name': 'lat'}, {'id': 'evlo', 'name': 'lon'}, {'id': 'evdp', 'name': 'dep'}, {'id': 'mag', 'name': 'mag'}, ], fixed_rows={'headers': True, 'data': 0}, style_cell={'width': '70px'}, sort_action='native', sort_mode='multi', row_selectable='single', row_deletable=False, filter_action='native', page_size=50, ) # ---- 04. phase catalog drop-down list phase_loc_layout = dcc.Dropdown(id='phase_loc_dw', options=[], placeholder='select an earthquake phase catalog', multi=True ) # ---- 04. station catalog drop-down list sta_loc_layout = dcc.Dropdown(id='sta_loc_dw', options=[], placeholder='select a station catalog', multi=True ) # ---- 06. continuous waveform path deployment_path = dcc.Dropdown(id='deployment_pt', options=[], placeholder='select deployment output path', multi=False ) # ---- 05. mode radio buttons mode_button = dcc.RadioItems( options=[ {'label': 'simultaneous', 'value': 'sim'}, {'label': 'manual', 'value': 'manual'} ] ) # ---- 06. mode switch mode_switch = daq.BooleanSwitch( id='mode_switch', on=False, label='Instant Update', labelPosition='top', ), # ---- 07. Icon nob icon_nob = daq.Knob( label="Display length (mins)", size=100, value=5, max=5, scale={'start': 0, 'labelInterval': 1, 'interval': 0.5}, id="win_nob" ) # ---- 08. Tabs tab_style = { 'borderBottom': '1px solid #d6d6d6', 'padding': '6px', 'fontWeight': 'bold' } tab_selected_style = { 'borderTop': '1px solid #d6d6d6', 'borderBottom': '1px solid #d6d6d6', 'backgroundColor': '#119DFF', 'color': 'white', 'padding': '6px' } idx_tabs = dcc.Tabs(id="index_page_tab", value='earthquakes', children=[ dcc.Tab(label='earthquakes', value='earthquakes', style=tab_style, selected_style=tab_selected_style), dcc.Tab(label='phases', value='phases', style=tab_style, selected_style=tab_selected_style), dcc.Tab(label='stations', value='stations', style=tab_style, selected_style=tab_selected_style) ]) # ---- 09. date-time picker cont_date_picker = dcc.DatePickerSingle( id='cont_dtp', display_format='M-D-Y', ) # ---- 10. time input cont_hours_input = daq.NumericInput( id='hour_inout', max=23, value=8, min=0, label='Hour' ) cont_minutes_input = daq.NumericInput( id='min_inout', max=59, value=20, min=0, label='Min' ) cont_seconds_input = daq.NumericInput( id='sec_inout', max=59, value=30, min=0, label='Sec' ) # ---- 11. Time range slider time_slider = dcc.RangeSlider( id="time_slider", min=0, max=86400, step=86400, value=[0, 86400], marks={i: f'{i}' for i in range(0, 86400, 10000) } ) # ---- button css # ---- 10.analysis button view_waveform_evt = html.A( id='view_event_wf', children=html.Button('View Event Waveform', type='submit', id='evtwf_button'), href='http://www.yahoo.com', target='_blank' ) # ---- 11. catalog analysis button view_events_sta = html.A( children=html.Button( 'Analyse Catalog', type='submit', id='evt_button'), id='ana_eq_cata', href='http://www.google.com', target='_blank' ) # ---- 12. view continuous data view_waveform_cont = html.A( children=html.Button( 'View All Waveform', type='submit', id='cont_button'), id='view_cont_wf', href='/apps/Continuous_WF', target='_blank', ) # ---- 13. radio item cont_filter_radio = dcc.RadioItems( options=[ {'label': 'raw', 'value': 'raw'}, {'label': 'bandpass', 'value': 'bandpass'}, {'label': 'highpass', 'value': 'highpass'}, {'label': 'lowpass', 'value': 'lowpass'}, ], value='bandpass', id='filter_type' ) # ---- 14. filer cont_filter_low = daq.NumericInput( id='filter_low', max=50, value=2, min=0.001, label='Low F', labelPosition='top', ) cont_filter_high = daq.NumericInput( id='filter_high', max=100, value=8, min=0.001, label='High F', labelPosition='top', ) # ---- normalization option cont_control_norm = dcc.Dropdown( id='norm_control', options=[ {'label': 'Original Scale', 'value': 'Original Scale'}, {'label': 'Normalize', 'value': 'Normalize'}, ], placeholder='select normalization style', multi=False, value="Normalize" ) # ---- waveform source option cont_wf_path = dcc.Dropdown(id='cont_wf_pt', options=[], placeholder='select continuous waveform path', multi=False ) # ---- 17. Tab control content Cont_control_tab = html.Div([ html.P("Settings for waveform display", className="cont_control_display_title"), html.Div([ html.P("Waveform Normalization Mode"), html.Div(cont_control_norm), html.P("Select Continuous Waveform Source"), html.Div(cont_wf_path), html.P('Select CPIC Deployment Source'), html.Div(deployment_path), ], className="cont_control_display_left") ]) # ---- 15. Continuous waveform display cont_wf_tabs = dcc.Tabs(id="cont_wf_tabs", value='Control', children=[ dcc.Tab(label='N', value='N', style=tab_style, selected_style=tab_selected_style), dcc.Tab(label='E', value='E', style=tab_style, selected_style=tab_selected_style), dcc.Tab(label='Z', value='Z', style=tab_style, selected_style=tab_selected_style), dcc.Tab(label='NEZ', value='NEZ', style=tab_style, selected_style=tab_selected_style), dcc.Tab(Cont_control_tab, label='Control', value='Control', style=tab_style, selected_style=tab_selected_style) ]) # ---- 16. cont joy stick browse_wf = daq.Joystick( id='move_handle', label="Browse waveform", angle=0, size=60, ), # ---- 18. Tab N control content Cont_N_tab = dcc.Loading( id='loadN', children=html.Div([dcc.Graph(id='N_comp_wfs')])) Cont_E_tab = dcc.Loading( id='loadE', children=html.Div([dcc.Graph(id='E_comp_wfs')])) Cont_Z_tab = dcc.Loading( id='loadZ', children=html.Div([dcc.Graph(id='Z_comp_wfs')])) Cont_NEZ_tab = dcc.Loading( id='loadNEZ', children=html.Div([dcc.Graph(id='NEZ_comp_wfs')]))

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0.23102

0.023398

0

1

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false

0.014652

0.021978

0

0.021978

0

null

0

null

0

1

0

faffb57b7651a65639929342efa031a9632c53ea

1,342

py

Python

Stable_baselines3/ppo_main2.py

FitMachineLearning/FitML

a60f49fce1799ca4b11b48307441325b6272719a

[ "MIT" ]

171

2017-11-07T09:59:20.000Z

2022-03-29T13:59:18.000Z

Stable_baselines3/ppo_main2.py

FitMachineLearning/FitML

a60f49fce1799ca4b11b48307441325b6272719a

[ "MIT" ]

1

2017-12-24T20:08:18.000Z

2018-01-31T22:26:49.000Z

Stable_baselines3/ppo_main2.py

FitMachineLearning/FitML

a60f49fce1799ca4b11b48307441325b6272719a

[ "MIT" ]

44

2017-11-07T12:08:05.000Z

2022-01-04T15:53:12.000Z

import gym import pybullet, pybullet_envs import torch as th from stable_baselines3 import PPO from stable_baselines3.common.evaluation import evaluate_policy # Create environment # env = gym.make('LunarLanderContinuous-v2') env = gym.make('BipedalWalker-v3') # env.render(mode="human") policy_kwargs = dict(activation_fn=th.nn.LeakyReLU, net_arch=[512, 512]) # Instantiate the agent model = PPO('MlpPolicy', env,learning_rate=0.0003,policy_kwargs=policy_kwargs, verbose=1) # Train the agent for i in range(8000): print("Training itteration ",i) model.learn(total_timesteps=10000) # Save the agent model.save("ppo_Ant") mean_reward, std_reward = evaluate_policy(model, model.get_env(), n_eval_episodes=5) print("mean_reward ", mean_reward) if mean_reward >= 270: print("***Agent Trained with average reward ", mean_reward) break del model # delete trained model to demonstrate loading # Load the trained agent # model = PPO.load("ppo_Ant") # Evaluate the agent # mean_reward, std_reward = evaluate_policy(model, model.get_env(), n_eval_episodes=10) # Enjoy trained agent # obs = env.reset() # for i in range(100): # action, _states = model.predict(obs, deterministic=True) # obs, rewards, dones, info = env.step(action) # env.render()

31.209302

90

0.707899

185

1,342

4.972973

0.513514

0.065217

0.043478

0.023913

0.134783

0

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0.182563

1,342

42

91

31.952381

0.807657

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0.166667

0

null

0

null

0

1

0

faffca2d14779955ce62c87fc83b42efbcaf3c11

5,886

py

Python

examples/_bak/testServer2.py

ProkopHapala/ProbeParticleModel

1afbd32cbf68440d71c2ee53f2066c898a00ae23

[ "MIT" ]

26

2015-10-21T21:02:03.000Z

2021-11-17T11:40:28.000Z

examples/_bak/testServer2.py

ProkopHapala/ProbeParticleModel

1afbd32cbf68440d71c2ee53f2066c898a00ae23

[ "MIT" ]

9

2015-10-26T14:11:25.000Z

2021-06-23T10:04:11.000Z

examples/_bak/testServer2.py

ProkopHapala/ProbeParticleModel

1afbd32cbf68440d71c2ee53f2066c898a00ae23

[ "MIT" ]

20

2015-07-13T14:39:59.000Z

2020-12-03T12:52:36.000Z

#!/usr/bin/python import matplotlib matplotlib.use('Agg') # Force matplotlib to not use any Xwindows backend. import os import sys import numpy as np import matplotlib.pyplot as plt import elements #print dir( elements ) import basUtils print(" # ========== make & load ProbeParticle C++ library ") def makeclean( ): import os [ os.remove(f) for f in os.listdir(".") if f.endswith(".so") ] [ os.remove(f) for f in os.listdir(".") if f.endswith(".o") ] [ os.remove(f) for f in os.listdir(".") if f.endswith(".pyc") ] #makeclean( ) # force to recompile import ProbeParticle as PP print(" # ========== server interface file I/O ") PP.loadParams( 'params.ini' ) print(" # ============ define atoms ") #bas = basUtils.loadBas('surf.bas')[0] #bas = basUtils.loadBas('PTCDA_Ruslan_1x1.bas')[0] #bas = basUtils.loadBas('GrN6x6.bas')[0] atoms = basUtils.loadAtoms('input.xyz') Rs = np.array([atoms[1],atoms[2],atoms[3]]); iZs = np.array( atoms[0]) if not PP.params['PBC' ]: print(" NO PBC => autoGeom ") PP.autoGeom( Rs, shiftXY=True, fitCell=True, border=3.0 ) print(" NO PBC => params[ 'gridA' ] ", PP.params[ 'gridA' ]) print(" NO PBC => params[ 'gridB' ] ", PP.params[ 'gridB' ]) print(" NO PBC => params[ 'gridC' ] ", PP.params[ 'gridC' ]) print(" NO PBC => params[ 'scanMin' ] ", PP.params[ 'scanMin' ]) print(" NO PBC => params[ 'scanMax' ] ", PP.params[ 'scanMax' ]) #Rs[0] += PP.params['moleculeShift' ][0] # shift molecule so that we sample reasonable part of potential #Rs[1] += PP.params['moleculeShift' ][1] #Rs[2] += PP.params['moleculeShift' ][2] Rs = np.transpose( Rs, (1,0) ).copy() Qs = np.array( atoms[4] ) if PP.params['PBC' ]: iZs,Rs,Qs = PP.PBCAtoms( iZs, Rs, Qs, avec=PP.params['gridA'], bvec=PP.params['gridB'] ) print("shape( Rs )", np.shape( Rs )); #print "Rs : ",Rs print(" # ============ define Scan and allocate arrays - do this before simulation, in case it will crash ") dz = PP.params['scanStep'][2] zTips = np.arange( PP.params['scanMin'][2], PP.params['scanMax'][2]+0.00001, dz )[::-1]; ntips = len(zTips); print(" zTips : ",zTips) rTips = np.zeros((ntips,3)) rs = np.zeros((ntips,3)) fs = np.zeros((ntips,3)) rTips[:,0] = 1.0 rTips[:,1] = 1.0 rTips[:,2] = zTips PP.setTip() xTips = np.arange( PP.params['scanMin'][0], PP.params['scanMax'][0]+0.00001, 0.1 ) yTips = np.arange( PP.params['scanMin'][1], PP.params['scanMax'][1]+0.00001, 0.1 ) extent=( xTips[0], xTips[-1], yTips[0], yTips[-1] ) fzs = np.zeros(( len(zTips), len(yTips ), len(xTips ) )); nslice = 10; atomTypesFile = os.path.dirname(sys.argv[0]) + '/../code/defaults/atomtypes.ini' FFparams = PP.loadSpecies( atomTypesFile ) C6,C12 = PP.getAtomsLJ( PP.params['probeType'], iZs, FFparams ) print(" # ============ define Grid ") cell =np.array([ PP.params['gridA'], PP.params['gridB'], PP.params['gridC'], ]).copy() gridN = PP.params['gridN'] FF = np.zeros( (gridN[2],gridN[1],gridN[0],3) ) #quit() # ============================================== # The costly part of simulation starts here # ============================================== print(" # =========== Sample LenardJones ") PP.setFF( FF, cell ) PP.setFF_Pointer( FF ) PP.getLenardJonesFF( Rs, C6, C12 ) plt.figure(figsize=( 5*nslice,5 )); plt.title( ' FF LJ ' ) ''' for i in range(nslice): plt.subplot( 1, nslice, i+1 ) plt.imshow( FF[i,:,:,2], origin='lower', interpolation='nearest' ) ''' withElectrostatics = ( abs( PP.params['charge'] )>0.001 ) if withElectrostatics: print(" # =========== Sample Coulomb ") FFel = np.zeros( np.shape( FF ) ) CoulombConst = -14.3996448915; # [ e^2 eV/A ] Qs *= CoulombConst #print Qs PP.setFF_Pointer( FFel ) PP.getCoulombFF ( Rs, Qs ) plt.figure(figsize=( 5*nslice,5 )); plt.title( ' FFel ' ) ''' for i in range(nslice): plt.subplot( 1, nslice, i+1 ) plt.imshow( FFel[i,:,:,2], origin='lower', interpolation='nearest' ) ''' FF += FFel*PP.params['charge'] PP.setFF_Pointer( FF ) del FFel ''' plt.figure(figsize=( 5*nslice,5 )); plt.title( ' FF total ' ) for i in range(nslice): plt.subplot( 1, nslice, i+1 ) plt.imshow( FF[i,:,:,2], origin='lower', interpolation='nearest' ) ''' print(" # ============ Relaxed Scan 3D ") for ix,x in enumerate( xTips ): print("relax ix:", ix) rTips[:,0] = x for iy,y in enumerate( yTips ): rTips[:,1] = y itrav = PP.relaxTipStroke( rTips, rs, fs ) / float( len(zTips) ) fzs[:,iy,ix] = fs[:,2].copy() #print itrav #if itrav > 100: # print " bad convergence > %i iterations per pixel " % itrav # print " exiting " # break print(" # ============ convert Fz -> df ") dfs = PP.Fz2df( fzs, dz = dz, k0 = PP.params['kCantilever'], f0=PP.params['f0Cantilever'], n=int(PP.params['Amplitude']/dz) ) print(" # ============ Plot Relaxed Scan 3D ") #slices = range( PP.params['plotSliceFrom'], PP.params['plotSliceTo'], PP.params['plotSliceBy'] ) #print "plotSliceFrom, plotSliceTo, plotSliceBy : ", PP.params['plotSliceFrom'], PP.params['plotSliceTo'], PP.params['plotSliceBy'] #print slices #nslice = len( slices ) slices = list(range( 0, len(dfs))) for ii,i in enumerate(slices): print(" plotting ", i) plt.figure( figsize=( 10,10 ) ) plt.imshow( dfs[i], origin='lower', interpolation=PP.params['imageInterpolation'], cmap=PP.params['colorscale'], extent=extent ) # z = zTips[i] - PP.params['moleculeShift' ][2] z = zTips[i] plt.colorbar(); plt.xlabel(r' Tip_x $\AA$') plt.ylabel(r' Tip_y $\AA$') plt.title( r"df Tip_z = %2.2f $\AA$" %z ) plt.savefig( 'df_%04i.png' %i, bbox_inches='tight' ) print(" ***** ALL DONE ***** ") #plt.show()

30.030612

132

0.578661

803

5,886

4.229141

0.298879

0.089517

0.017668

0.023557

0.201413

0.168139

0.158422

0.148999

0.128975

0

0.028101

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5,886

195

133

30.184615

0.689415

0.185525

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0.039216

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0.098039

0.205882

0

null

0

null

0

1

0

4f019e58d978897b9b1cc1a2f377de443998fabc

820

py

Python

create_table.py

juliachn/astronaut-db

ac1e1df5d109b5975548d8578f535ca8b2bedf39

[ "MIT" ]

null

create_table.py

juliachn/astronaut-db

ac1e1df5d109b5975548d8578f535ca8b2bedf39

[ "MIT" ]

null

create_table.py

juliachn/astronaut-db

ac1e1df5d109b5975548d8578f535ca8b2bedf39

[ "MIT" ]

null

"""create table in postgresql""" from config import config import psycopg2 def create_tables(): sql = """CREATE TABLE astronauts_in_space (name VARCHAR(255) NOT NULL, craft VARCHAR(20) NOT NULL)""" conn = None try: # read connection parameters params = config() # connect to the PostgreSQL server conn = psycopg2.connect(**params) # create a new cursor cur = conn.cursor() # execute the create_table statement cur.execute(sql) # close communication with the db cur.close() # commit the changes conn.commit() except (Exception, psycopg2.DatabaseError) as error: print(error) finally: if conn is not None: conn.close() if __name__ == '__main__': create_tables()

23.428571

75

0.608537

94

820

5.170213

0.56383

0.067901

0

0.014011

0.303659

820

35

76

23.428571

0.837128

0.235366

0

0.165316

0

1

0.05

false

0

0.1

0

0.15

0.05

0

null

0

null

0

1

0

4f02a1b01f31096708b76acaf22f14eb038ba6d3

2,025

py

Python

tests/fitness/observation_fitness_test.py

auxein/auxein

5388cb572b65aecc282f915515c35dc3b987154c

[ "Apache-2.0" ]

1

2019-05-08T14:53:27.000Z

tests/fitness/observation_fitness_test.py

auxein/auxein

5388cb572b65aecc282f915515c35dc3b987154c

[ "Apache-2.0" ]

2

2020-08-26T09:16:47.000Z

2020-10-30T16:47:03.000Z

tests/fitness/observation_fitness_test.py

auxein/auxein

5388cb572b65aecc282f915515c35dc3b987154c

[ "Apache-2.0" ]

null

import numpy as np from auxein.population import build_individual from auxein.fitness.observation_based import ObservationBasedFitness, MultipleLinearRegression, MaximumLikelihood def test_multiple_linear_regression(): xs = np.array([[23], [26], [30], [34], [43], [48], [52], [57], [58]]) y = np.array([651, 762, 856, 1063, 1190, 1298, 1421, 1440, 1518]) i = build_individual([23.42, 167.68], []) fitness_function = MultipleLinearRegression(xs, y) assert np.isclose(fitness_function.fitness(i), -18804) def test_fitness_landscape(): class TestFitnessFunction(ObservationBasedFitness): def fitness(self, individual): return individual.genotype.dna[0] + individual.genotype.dna[1] def value(self, individual, x): pass fitness_function = TestFitnessFunction() landscape = fitness_function.get_landscape([[-1, 1], [0, 1]], 3) assert len(landscape) == 9 for e in landscape: assert len(e) == 3 expected = [[-1, 0, -1], [0, 0, 0], [1, 0, 1], [-1, 0.5, -0.5], [0, 0.5, 0.5], [1, 0.5, 1.5], [-1, 1, 0], [0, 1, 1], [1, 1, 2]] assert np.array_equal(landscape, expected) # Classic example with students and time spent studying # from: https://en.wikipedia.org/wiki/Logistic_regression def test_maximum_likelihood_value(): xs = np.array([[0.50], [0.75], [1.00], [1.25], [1.50], [1.75], [1.75], [2.00], [2.25], [2.50], [2.75], [3.00], [3.25], [3.50], [4.00], [4.25], [4.50], [4.75], [5.00], [5.50]]) y = np.array([0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1]) fitness_function = MaximumLikelihood(xs, y) i = build_individual([-4.0777, 1.5046]) assert np.isclose(fitness_function.value(i, [1]), 0.07, atol=0.01) assert np.isclose(fitness_function.value(i, [2]), 0.26, atol=0.01) assert np.isclose(fitness_function.value(i, [3]), 0.61, atol=0.01) assert np.isclose(fitness_function.value(i, [4]), 0.87, atol=0.01) assert np.isclose(fitness_function.value(i, [5]), 0.97, atol=0.01)

42.1875

179

0.628642

323

2,025

3.863777

0.312694

0.020833

0.016827

0.105769

0.217147

0.184295

0.155449

0.137821

0

0.139005

0.175802

2,025

47

180

43.085106

0.608748

0.053827

0

0.28125

1

0.15625

false

0.03125

0.09375

0.03125

0.3125

0

null

0

null

0

1

0

4f03bab4ed15742dd70074c8d48e92b23ae5cb42

418

py

Python

server/accounts/urls.py

Vamshi399/project_management_tool

658af1c87b18d5e76e34c0b48ccf418c9f353423

[ "Apache-2.0" ]

null

server/accounts/urls.py

Vamshi399/project_management_tool

658af1c87b18d5e76e34c0b48ccf418c9f353423

[ "Apache-2.0" ]

null

server/accounts/urls.py

Vamshi399/project_management_tool

658af1c87b18d5e76e34c0b48ccf418c9f353423

[ "Apache-2.0" ]

1

2021-05-12T19:08:52.000Z

from django.conf.urls import url from . import views urlpatterns = [ url('api/users', views.UserCreate.as_view(), name='account-create'), url('tasks', views.TaskCreate.as_view(), name='tasks-create'), url('tasks2', views.Task2Create.as_view(), name='tasks-create'), url('project', views.ProjectCreate.as_view(), name='project-create'), url('role', views.RoleView.as_view(), name='role-view'), ]

32.153846

73

0.684211

56

418

5.017857

0.428571

0.106762

0.177936

0.106762

0.170819

0

0.005435

0.119617

418

12

74

34.833333

0.758152

0

0.220096

0

1

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false

0

0.222222

0

0.222222

0

null

0

null

0

1

0

4f06558b33e9c97342f7c648ce6deb597ed13453

1,739

py

Python

highbrow/posts/routes.py

ishmamt/highbrow

4fb8523d8c4cbc7d66bfecb61f40b75beca55eb3

[ "MIT" ]

null

highbrow/posts/routes.py

ishmamt/highbrow

4fb8523d8c4cbc7d66bfecb61f40b75beca55eb3

[ "MIT" ]

null

highbrow/posts/routes.py

ishmamt/highbrow

4fb8523d8c4cbc7d66bfecb61f40b75beca55eb3

[ "MIT" ]

null

from flask import render_template, url_for, redirect, request, Blueprint from highbrow.posts.forms import PostForm from highbrow.posts.utils import fetch_post, create_comment, fetch_comments, like_unlike_post from highbrow.utils import fetch_notifications, if_is_liked, if_is_saved from flask_login import current_user posts = Blueprint('posts', __name__) # similar to app = Flask(__name__) @posts.route("/post/<string:post_id>", methods=["GET", "POST"]) def post(post_id): post = fetch_post(post_id) comments = fetch_comments(post_id) notifications = fetch_notifications(current_user.username) is_liked = if_is_liked(current_user.username, post_id) is_saved = if_is_saved(current_user.username, post_id) profile_picture = url_for('static', filename='profile_pictures/' + current_user.profile_picture) comment_form = PostForm() if comment_form.validate_on_submit() and request.method == "POST": create_comment(current_user.username, post["link"], post["username"], comment_form.comment.data) return redirect(url_for('posts.post', post_id=post_id)) return render_template("post.html", comment_form=comment_form, comments=comments, number_of_comments=post["comments"], post_details=post, notifications=notifications, current_user=current_user.username, is_liked=is_liked, is_saved=is_saved, profile_picture=profile_picture) @posts.route("/post/like/<string:notified_user>/<string:notifying_user>/<string:post_id>/<string:is_liked>") def like_post(notifying_user, notified_user, post_id, is_liked): like_unlike_post(notifying_user, notified_user, post_id, is_liked) return redirect(url_for("posts.post", post_id=post_id))

56.096774

133

0.761357

240

1,739

5.154167

0.254167

0.063056

0.076799

0.033953

0.216653

0.134196

0.066289

0

0.130535

1,739

30

134

57.966667

0.818122

0.018401

0

0.04

0.118475

0.066862

0

1

0.08

false

0

0.2

0

0.4

0.08

0

null

0

null

0

1

0

4f0bb6996176372b6cd900111be1f18d7f46c073

2,319

py

Python

configs/ufld/resnet18_tusimple.py

AbdulFMS/lanedet

32f258437f1852a115d194a3c195913a7d65240c

[ "Apache-2.0" ]

277

2021-04-16T02:49:52.000Z

2022-03-31T06:25:11.000Z

configs/ufld/resnet18_tusimple.py

ibaiGorordo/lanedet

210709066886e4906c8d3ab4d9173785ef07c65d

[ "Apache-2.0" ]

40

2021-05-17T08:31:48.000Z

2022-03-31T11:39:21.000Z

configs/ufld/resnet18_tusimple.py

ibaiGorordo/lanedet

210709066886e4906c8d3ab4d9173785ef07c65d

[ "Apache-2.0" ]

47

2021-04-16T07:18:53.000Z

2022-03-17T03:13:21.000Z

net = dict( type='Detector', ) backbone = dict( type='ResNetWrapper', resnet='resnet18', pretrained=True, replace_stride_with_dilation=[False, False, False], out_conv=False, ) featuremap_out_channel = 512 griding_num = 100 num_classes = 6 heads = dict(type='LaneCls', dim = (griding_num + 1, 56, num_classes)) trainer = dict( type='LaneCls' ) evaluator = dict( type='Tusimple', ) import math scheduler = dict( type = 'LambdaLR', lr_lambda = lambda _iter : math.pow(1 - _iter/total_iter, 0.9) ) optimizer = dict( type = 'SGD', lr = 0.025, weight_decay = 1e-4, momentum = 0.9 ) epochs = 150 batch_size = 4 total_iter = (3616 // batch_size + 1) * epochs import math scheduler = dict( type = 'LambdaLR', lr_lambda = lambda _iter : math.pow(1 - _iter/total_iter, 0.9) ) img_norm = dict( mean=[103.939, 116.779, 123.68], std=[1., 1., 1.] ) ori_img_h = 720 ori_img_w = 1280 img_h = 288 img_w = 800 cut_height=0 sample_y = range(710, 150, -10) dataset_type = 'TuSimple' dataset_path = './data/tusimple' row_anchor = 'tusimple_row_anchor' train_process = [ dict(type='RandomRotation', degree=(-6, 6)), dict(type='RandomUDoffsetLABEL', max_offset=100), dict(type='RandomLROffsetLABEL', max_offset=200), dict(type='GenerateLaneCls', row_anchor=row_anchor, num_cols=griding_num, num_classes=num_classes), dict(type='Resize', size=(img_w, img_h)), dict(type='Normalize', img_norm=img_norm), dict(type='ToTensor', keys=['img', 'cls_label']), ] val_process = [ dict(type='Resize', size=(img_w, img_h)), dict(type='Normalize', img_norm=img_norm), dict(type='ToTensor', keys=['img']), ] dataset = dict( train=dict( type=dataset_type, data_root=dataset_path, split='trainval', processes=train_process, ), val=dict( type=dataset_type, data_root=dataset_path, split='test', processes=val_process, ), test=dict( type=dataset_type, data_root=dataset_path, split='test', processes=val_process, ) ) workers = 12 ignore_label = 255 log_interval = 100 eval_ep = 1 save_ep = epochs row_anchor='tusimple_row_anchor' test_json_file='data/tusimple/test_label.json' lr_update_by_epoch = False

20.522124

66

0.651574

317

2,319

4.514196

0.388013

0.1174

0.023061

0.039832

0.37666

0.340321

0.310273

0

0.05117

0.207848

2,319

112

67

20.705357

0.727817

0

0.270833

0

0.131091

0.012505

0

1

0

false

0

0.020833

0

0.020833

0

null

0

null

0

1

0

4f0c86d2283f684984e36164a05189394bd02f11

334

py

Python

Experiencing-Python/HackerRank/Capitalize!.py

ar-pavel/Code-Library

2d1b952231c1059bbf98d85d2c23fd8fb21b455c

[ "MIT" ]

null

Experiencing-Python/HackerRank/Capitalize!.py

ar-pavel/Code-Library

2d1b952231c1059bbf98d85d2c23fd8fb21b455c

[ "MIT" ]

null

Experiencing-Python/HackerRank/Capitalize!.py

ar-pavel/Code-Library

2d1b952231c1059bbf98d85d2c23fd8fb21b455c

[ "MIT" ]

null

def solve(s): t=" " c = "" for i in s: if t == ' ': c += i.upper() else: c += i t = i return c if __name__ == '__main__': fptr = open(os.environ['OUTPUT_PATH'], 'w') s = input() result = solve(s) fptr.write(result + '\n') fptr.close()

15.181818

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0.619048

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0

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334

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0.125

0

null

0

null

0

1

0

4f0f3484a16e736e3a84e167b7d1c89961787b7d

4,429

py

Python

overlay.py

blazejmanczak/AoM-LineMatching

1c81fd1dd396e3cc120d5bab388acc92181e2881

[ "Apache-2.0" ]

2

2021-03-01T06:18:13.000Z

2021-05-18T11:33:30.000Z

overlay.py

blazejmanczak/ArtifactsOfMemory

1c81fd1dd396e3cc120d5bab388acc92181e2881

[ "Apache-2.0" ]

null

overlay.py

blazejmanczak/ArtifactsOfMemory

1c81fd1dd396e3cc120d5bab388acc92181e2881

[ "Apache-2.0" ]

null

# Copyright 2020-present, Netherlands Institute for Sound and Vision (Blazej Manczak) # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. ############################################################################## from matchingMethods import all_in_one import argparse import os import pandas as pd from PIL import Image import time def print_config(): """ Prints all entries in config variable. """ print("[INFO]: Overlaying with follwoing parameters ...") for key, value in vars(config).items(): print(key + ' : ' + str(value)) def overaly(config): """Performs the overlaying""" print("[INFO]: Loading in the pickeled data ... ") img_names = os.listdir(config.path_dir, ) img_paths =[os.path.join(config.path_dir, name) for name in img_names] data = pd.read_pickle(config.data_directory) non_zero_objects_dic = pd.read_pickle(config.non_zero_objects_dic_directory) threshold, minLineLength, maxLineGap = [int(param) for param in config.hough_params.split(",")] # parse hough parameters start_time = time.time() count = 0 for img_path in img_paths: try: img_array = all_in_one(path = img_path, data = data, non_zero_objects_dic = non_zero_objects_dic ,num_lines = config.num_lines, normalizing_stats=[71.73, 26.70, 254.71, 94.19], params_hough={"threshold": threshold, "minLineLength": minLineLength, "maxLineGap": maxLineGap}) im = Image.fromarray(img_array) im.save(os.path.join(config.save_dir ,"overlayed_" + img_path.split("/")[-1])) count += 1 except Exception as e: print("Overlaying failed for path {} with exception {} ".format(img_path, e)) end_time = time.time() print("[INFO]: overalying and saving took on average {} seconds per query image".format(round((end_time-start_time)/count,4))) if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("--path_dir", required=False, type=str, default = "frames/contemporary" , help="Directory containing images on which the matching should be done. All images in the directory will be matched. The directory should contain only images.") parser.add_argument("--save_dir", required=False, type=str, default="frames/outputs", help="Directory where the overlayed images should be stored.") parser.add_argument("--data_directory", required = False, type = str, default = "data/data.pkl", help = "Diectory to a pickle file of the processed archives") parser.add_argument("--non_zero_objects_dic_directory", required=False, type=str, default="data/non_zero_object_dic.pickle", help="Diectory to a pickle file of the processed matchingObjects that contain a line") parser.add_argument("--num_lines", required=False, type=int, default=1, help="How many lines should be overlayed? If num_lines bigger than matches, all matches are overlayed.") parser.add_argument("--hough_params", required=False, type=str, default="200,150,25", help="What parameters to use for line detection? Argument is expected to be a string of integers seperated by a comma. \ Consecutive ints stand for threshold, minLineLength and maxLineGap respectively.") config = parser.parse_args() print_config() overaly(config) print("[INFO]: Overlaying successful!")

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0.121212

0

null

0

null

0

1

0

877c1d4a6e53fdc3a180cca9ff5ee364a4423ed1

2,392

py

Python

mabel/data/formats/json.py

mabel-dev/mabel

ee1fdfcfe5fb87d2c5ce4f24b4b7113478ba1b8a

[ "Apache-2.0" ]

null

mabel/data/formats/json.py

mabel-dev/mabel

ee1fdfcfe5fb87d2c5ce4f24b4b7113478ba1b8a

[ "Apache-2.0" ]

287

2021-05-14T21:25:26.000Z

2022-03-30T12:02:51.000Z

mabel/data/formats/json.py

mabel-dev/mabel

ee1fdfcfe5fb87d2c5ce4f24b4b7113478ba1b8a

[ "Apache-2.0" ]

1

2021-04-29T18:18:20.000Z

""" Create .serialize and .parse methods to handle json operations Where orjson is installed, the performance impact is nil, without orjson, parsing is about as fast as ujson, however serialization is slower, although still faster than the native json library. """ from typing import Any, Union import datetime try: # if orjson is available, use it import orjson parse = orjson.loads def serialize( obj: Any, indent: bool = False, as_bytes: bool = False ) -> Union[str, bytes]: if as_bytes: if indent and isinstance(obj, dict): return orjson.dumps( obj, option=orjson.OPT_INDENT_2 + orjson.OPT_SORT_KEYS ) else: return orjson.dumps(obj, option=orjson.OPT_SORT_KEYS) # return a string if indent and isinstance(obj, dict): return orjson.dumps( obj, option=orjson.OPT_INDENT_2 + orjson.OPT_SORT_KEYS ).decode() else: return orjson.dumps(obj, option=orjson.OPT_SORT_KEYS).decode() except ImportError: # pragma: no cover # orjson doesn't install on 32bit systems so we need a backup plan # however, orjson and ujson have functional differences so we can't # just swap the references. import ujson def serialize( obj: Any, indent: bool = False, as_bytes: bool = False ) -> Union[str, bytes]: # type:ignore def fix_fields(dt: Any) -> str: """ orjson and ujson handles some fields differently, if one of those fields is detected, fix it. """ if isinstance(dt, (datetime.date, datetime.datetime)): return dt.isoformat() if isinstance(dt, dict): return fix_fields(dt) return dt if isinstance(obj, dict): obj_copy = {k: fix_fields(v) for k, v in obj.items()} else: obj_copy = obj if as_bytes: if indent: return ujson.dumps(obj_copy, sort_keys=True, indent=2).encode() else: return ujson.dumps(obj_copy, sort_keys=True).encode() if indent: return ujson.dumps(obj_copy, sort_keys=True, indent=2) else: return ujson.dumps(obj_copy, sort_keys=True) parse = ujson.loads # type:ignore

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2,392

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0.25

0

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false

0

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0

0.413043

0

null

0

null

0

1

0

877cb1febc5d5fc104596b7224e3703948c81dbb

15,191

py

Python

autograder.py

B1ACK917/2021HWAutoGrader

a76663bc596a1e6b190a29d7aa38c974dd536819

[ "MIT" ]

81

2021-03-13T04:16:27.000Z

2021-05-13T03:17:48.000Z

autograder.py

wslyw/2021HWAutoGrader

a76663bc596a1e6b190a29d7aa38c974dd536819

[ "MIT" ]

12

2021-03-14T11:32:06.000Z

2021-03-26T05:28:26.000Z

autograder.py

wslyw/2021HWAutoGrader

a76663bc596a1e6b190a29d7aa38c974dd536819

[ "MIT" ]

30

2021-03-14T15:28:18.000Z

2022-03-26T01:47:08.000Z

""" @Version 0.3.3 @Author B1ACK917 @Contributor YuanWind """ import json import os from tqdm import tqdm import time import copy import matplotlib.pyplot as plt from genHTML import gen import platform def check_bomb(server, VMList, serverDict, VMDict, serverIDMap, VMIDMap, vmid2node): """ 判断一个服务器是否发生资源溢出 :param server: 服务器ID :param VMList: 服务器上挂载的虚拟机 :param serverDict: 服务器型号到服务器详细信息的映射 :param VMDict: 虚拟机ID到虚拟机详细信息的映射 :param serverIDMap: 从服务器ID到服务器型号的映射 :param VMIDMap: 虚拟机ID到虚拟机型号的映射 :param vmid2node: 虚拟机节点信息 :return: """ serverCPU, serverMEM = serverDict[serverIDMap[server]]['cpu'], serverDict[serverIDMap[server]]['memory'] serverCPU_A, serverMEM_A = serverCPU / 2, serverMEM / 2 serverCPU_B, serverMEM_B = serverCPU / 2, serverMEM / 2 for VM in VMList: node = vmid2node[VM] if node == 'A': serverCPU_A -= VMDict[VMIDMap[VM]]['cpu'] serverMEM_A -= VMDict[VMIDMap[VM]]['memory'] elif node == 'B': serverCPU_B -= VMDict[VMIDMap[VM]]['cpu'] serverMEM_B -= VMDict[VMIDMap[VM]]['memory'] elif node == None: serverCPU_A -= VMDict[VMIDMap[VM]]['cpu'] / 2 serverMEM_A -= VMDict[VMIDMap[VM]]['memory'] / 2 serverCPU_B -= VMDict[VMIDMap[VM]]['cpu'] / 2 serverMEM_B -= VMDict[VMIDMap[VM]]['memory'] / 2 if serverCPU_A < 0 or serverMEM_A < 0 or serverCPU_B < 0 or serverMEM_B < 0: return False return True def grader(testCmd, ioData): vmid2node = {} serverDict = {} VMDict = {} operateInfo = [] """ 所有处理后的数据将存放在下文中的fullInfo中，每一个字段在后面的注释都有说明。 """ with open(ioData) as file: """ 从输入文件中获取服务器信息和每一天的操作序列 operateInfo中按天存储了当天的操作序列 """ serverNums = eval(file.readline()) for i in range(serverNums): _type, _cpus, _mem, _hardCost, _energyCost = file.readline()[1:-2].split(',') serverDict.update({_type: {'cpu': eval(_cpus), 'memory': eval(_mem), 'hardCost': eval(_hardCost), 'energyCost': eval(_energyCost)}}) VMNums = eval(file.readline()) for i in range(VMNums): _type, _cpus, _mem, doubleNode = file.readline()[1:-2].split(',') VMDict.update({_type: {'cpu': eval(_cpus), 'memory': eval(_mem), 'double': eval(doubleNode)}}) days = eval(file.readline()) for i in range(days): dayOperateInfo = {'operate': []} nums = eval(file.readline()) for j in range(nums): tmp = file.readline().strip()[1:-1] if tmp[:3] == 'add': _op, _, _id = tmp.split(',') dayOperateInfo['operate'].append((_op, eval(_id), _)) else: _op, _id = tmp.split(',') dayOperateInfo['operate'].append((_op, eval(_id))) operateInfo.append(dayOperateInfo) beginTime = time.perf_counter() result = os.popen(testCmd) result = result.read().strip().split('\n') endTime = time.perf_counter() # 计时器 fullInfo = [] for i in range(len(result)): if 'purchase' in result[i]: # 整理每天的购买信息 singleDayInfo = {'purchase': {}} _, serverBought = result[i][1:-1].split(',') serverBought = eval(serverBought.strip()) for j in range(i + 1, i + serverBought + 1): serverName, serverNum = result[j][1:-1].split(',') singleDayInfo['purchase'].update({serverName: eval(serverNum)}) fullInfo.append(singleDayInfo) elif 'migration' in result[i]: # 整理每天的迁移信息 migrationInfo = {'migration': []} _, migrationNum = result[i][1:-1].split(',') migrationNum = eval(migrationNum.strip()) for j in range(i + 1, i + migrationNum + 1): sp = result[j][1:-1].split(',') if len(sp) == 2: sourceID, targetID = sp migrationInfo['migration'].append((eval(sourceID), (eval(targetID), None))) else: sourceID, targetID, targetNode = sp migrationInfo['migration'].append((eval(sourceID), (eval(targetID), targetNode))) fullInfo[-1].update(migrationInfo) requestInfo = {'request': []} for j in range(i + migrationNum + 1, len(result)): # 超过迁移行数以后的部分被认为是部署信息，直到遇到purchase为止 if 'purchase' in result[j]: break sp = result[j][1:-1].split(',') if len(sp) == 1: serverID, serverNode = sp[0], None requestInfo['request'].append((eval(serverID), serverNode)) else: serverID, serverNode = sp requestInfo['request'].append((eval(serverID), serverNode.strip())) fullInfo[-1].update(requestInfo) for i in range(len(fullInfo)): fullInfo[i].update(operateInfo[i]) serverIDMap = {} IDInd = 0 dayServerInfo = {} VMIDMap = {} VMIDTypeMap = {} migTot = 0 migHappenTime = [] bombInfo = [] migOverInfo = [] for day_i in range(len(fullInfo)): day = fullInfo[day_i] for server in day['purchase']: for i in range(day['purchase'][server]): serverIDMap.update({IDInd: server}) dayServerInfo[IDInd] = [] IDInd += 1 cnt = 0 for mig in day['migration']: try: cnt += 1 source = mig[0] target = mig[1][0] vmid2node[source] = mig[1][1] dayServerInfo[VMIDMap[source]].remove(source) dayServerInfo[target].append(source) VMIDMap[source] = target migTot += 1 if cnt > int(len(VMIDMap) / 200): migOverInfo.append(('migOverflow', day_i + 1, cnt, mig, int(len(VMIDMap) / 200))) if not check_bomb(target, dayServerInfo[target], serverDict, VMDict, serverIDMap, VMIDTypeMap, vmid2node): bombInfo.append( ('Migration', day_i + 1, cnt, mig, target, serverIDMap[target], serverDict[serverIDMap[target]], dayServerInfo[target][-5 if len( dayServerInfo[target]) > 5 else -len(dayServerInfo[target]):])) except KeyError: raise RuntimeError(('migration error', mig)) migHappenTime.append(cnt) opInd = 0 for op in day['operate']: if op[0] == 'add': try: dayServerInfo[day['request'][opInd][0]].append(op[1]) VMIDMap[op[1]] = day['request'][opInd][0] vmid2node[op[1]] = day['request'][opInd][1] VMIDTypeMap[op[1]] = op[2].strip() if not check_bomb(day['request'][opInd][0], dayServerInfo[day['request'][opInd][0]], serverDict, VMDict, serverIDMap, VMIDTypeMap, vmid2node): bombInfo.append(('Add', day_i + 1, opInd + 1, op, day['request'][opInd][0], serverIDMap[day['request'][opInd][0]], serverDict[serverIDMap[day['request'][opInd][0]]], dayServerInfo[day['request'][opInd][0]][-5 if len( dayServerInfo[day['request'][opInd][0]]) > 5 else -len( dayServerInfo[day['request'][opInd][0]]):])) opInd += 1 except KeyError: raise RuntimeError(('server plant error', day['request'][opInd][0], (op[0], op[2], op[1]))) except IndexError: raise RuntimeError(('req error', (op[0], op[2], op[1]))) else: dayServerInfo[VMIDMap[op[1]]].remove(op[1]) vmid2node[op[1]] = 'del' # try: # # except KeyError: # raise RuntimeError(('server plant error', VMIDMap[op[1]], op)) fullInfo[day_i].update({'info': copy.deepcopy(dayServerInfo)}) """ 经过以上操作后，所有的信息都将被存储到fullInfo中，获取信息可以按照以下方式： fullInfo['purchase']：长度为天数的一个列表，每一个元素是当天的购买信息，以键值对存储。比如{'SERVER1':40}表示购买了40台SERVER1型号服务器。 fullInfo['migration']：长度为天数的一个列表，每一个元素是当天的迁移信息，迁移信息存储在列表中，每一个迁移操作以元组形式存储。比如(12345,10,None)表示将12345号虚拟机迁移到10号服务器上，以双节点部署。又比如(12345,10,A)则表示将12345号虚拟机迁移到10号服务器上的A节点。 fullInfo['operate']：长度为天数的一个列表，每一个元素是当天的操作信息，包括add和delete，存储在列表中，每一个操作信息以元组形式存储如果元组第一个元素为'add'，则元组长度为3，分别为('add',虚拟机ID,虚拟机型号) 如果元组第一个元素为'del'，则元组长度为2，分别为('del',虚拟机ID) fullInfo['request']：长度为天数的一个列表，每一个元素是当天的部署情况，以元组形式存储。比如(4,A)表示将对应请求add的虚拟机部署到4号服务器的A节点上。如果元组第二个元素即tuple[1]为None，则表示虚拟机双节点部署到该服务器上。 fullInfo['info']：长度为天数的一个列表，每一个元素是当天结束时服务器以及挂载在其上的虚拟机，以键值对形式存储。比如{5:[123,321,345,654]}表示5号服务器上挂载了4台虚拟机，ID分别为123，321，345，654 """ energyCost = [] emptyRate = [] hardCost = 0.0 serverNums = {} for serverID, _ in dayServerInfo.items(): """ 计算硬件成本，从最后一天的服务器信息中统计。 """ hardCost += serverDict[serverIDMap[serverID]]['hardCost'] if serverIDMap[serverID] not in serverNums: serverNums[serverIDMap[serverID]] = 1 else: serverNums[serverIDMap[serverID]] += 1 for day in fullInfo: """ 计算每日运行成本。计算方式是每天结束时如果一台服务器上有虚拟机在挂载状态，就计算一天的运行费用。同时统计闲置率。 """ info = day['info'] c = 0.0 inUse, empty = 0, 0 for serverID, _ in info.items(): if _: c += serverDict[serverIDMap[serverID]]['energyCost'] inUse += 1 else: empty += 1 energyCost.append(c) emptyRate.append((empty / (inUse + empty)) if inUse + empty else 0) timeFormat = '%m_%d_%H_%M_%S' folderName = os.path.join('./resource', time.strftime(timeFormat, time.localtime(time.time()))) os.mkdir(folderName) plt.rcParams['font.sans-serif'] = ['SimHei'] plt.rcParams['axes.unicode_minus'] = False plt.plot(range(len(fullInfo)), emptyRate, label='Empty Ratio') plt.xlabel('Days') plt.ylabel('Ratio') plt.title('Empty Ratio (How many servers are not in use)') plt.legend() plt.savefig(os.path.join(folderName, '1.png')) plt.clf() plt.plot(range(len(fullInfo)), energyCost, label='Energy Cost') plt.xlabel('Days') plt.ylabel('Money') plt.title('Energy Cost (When a server is in use, it leads to energy cost)') plt.legend() plt.savefig(os.path.join(folderName, '2.png')) plt.clf() plt.plot(range(len(fullInfo)), migHappenTime, label='Migration Times') plt.xlabel('Days') plt.ylabel('Times') plt.title('Migration Times') plt.legend() plt.savefig(os.path.join(folderName, '3.png')) plt.clf() labels = ['{}\n{}cpu\n{}mem'.format(s, serverDict[s]['cpu'], serverDict[s]['memory']) for s in serverNums.keys()] sizes = list(serverNums.values()) plt.pie(sizes, labels=labels, autopct='%1.2f%%') plt.title('Server Types') plt.savefig(os.path.join(folderName, '4.png')) plt.clf() return os.path.split(ioData)[-1], hardCost, sum(energyCost), endTime - beginTime, sum(emptyRate) / len( emptyRate), sum(energyCost) / len(energyCost), folderName, migTot, bombInfo, migOverInfo if __name__ == '__main__': if not os.path.exists('./resource'): os.mkdir('./resource') l = [] with open('config.json') as file: config = json.load(file) language = config['language'] pypyPath = config['pythonInterpreter'] exe = config['executable'] sourceCode = config['sourceCode'] javaPath = config['javaPath'] javaJARFile = config['buildJARPath'] ioDataList = config['ioData'] # 从config获取参数 print('AutoGrader Running with args: {}'.format( [language, pypyPath, exe, sourceCode, javaPath, javaJARFile, ioDataList])) for d in tqdm(ioDataList, ncols=40): """ 根据语言生成测试指令testCmd """ if language == 'c' or language == 'c++': testCmd = '\"{}\"<\"{}\"'.format(exe, d) elif language == 'python': if pypyPath: testCmd = '\"{}\" \"{}\"<\"{}\"'.format(pypyPath, sourceCode, d) else: testCmd = 'python \"{}\"<\"{}\"'.format(sourceCode, d) elif language == 'java': filePath, JARPath = os.path.split(javaJARFile) if javaPath: testCmd = '\"{}\" -Djava.library.path=\"{}\" -classpath \"{}\" \"com.huawei.java.main.Main\"<\"{}\"'.format( javaPath, filePath, javaJARFile, d) else: testCmd = 'java -Djava.library.path=\"{}\" -classpath \"{}\" \"com.huawei.java.main.Main\"<\"{}\"'.format( filePath, javaJARFile, d) else: raise ValueError('unsupport language') try: _ = grader(testCmd, d) l.append(_) except RuntimeError as e: if e.args[0][0] == 'server plant error': print('服务器或虚拟机信息错误,服务器 ID 不存在') print('发生错误的服务器ID为{}，你输出的操作为{}'.format(e.args[0][1], e.args[0][2])) elif e.args[0][0] == 'migration error': print('虚拟机迁移错误,服务器 ID 不存在') print('你输出的操作为{}'.format(e.args[0][1])) elif e.args[0][0] == 'req error': print('请求错误') print('找不到和{}对应的服务器部署操作'.format(e.args[0][1])) print('该问题导致分析器无法继续运行，报告中不会包含本次分析') res = gen(l) sys = platform.system() if sys == 'Windows' or sys == 'windows': os.popen('start chrome.exe {}'.format(os.path.join(os.path.dirname(os.path.abspath(__file__)), res))) elif sys == 'Linux' or sys == 'linux': os.popen('google-chrome {}'.format(os.path.join(os.path.dirname(os.path.abspath(__file__)), res))) print('你正在使用Linux系统，可能无法打开网页或报错，请尝试用默认浏览器打开目录下最新生成的html或者将html和resource拷贝到Windows下查看\n') elif sys == 'Darwin' or sys == 'darwin': os.popen('open -a Safari {}'.format(os.path.join(os.path.dirname(os.path.abspath(__file__)), res))) print('你正在使用Mac系统，可能无法打开网页或报错，请尝试用默认浏览器打开目录下最新生成的html或者将html和resource拷贝到Windows下查看\n')

42.314763

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0.529853

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0.022099

0.262557

0.211326

0.148543

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0.061652

0.044576

0

0.020516

0.32618

15,191

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0

0.101083

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0

1

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false

0

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0.036101

0

null

0

null

0

1

0

877dbaa8ca2f80ac5b136fa5a55cc2df0dab1b58

1,215

py

Python

my_life/lifeServerApiApp/App/DiaryService/diaryApi.py

CLAY-zhao/MyLife

994e1f4b2cb20b0bd09edc95ea5ed0b09010a1e3

[ "bzip2-1.0.6" ]

null

my_life/lifeServerApiApp/App/DiaryService/diaryApi.py

CLAY-zhao/MyLife

994e1f4b2cb20b0bd09edc95ea5ed0b09010a1e3

[ "bzip2-1.0.6" ]

1

2022-01-15T05:36:51.000Z

my_life/lifeServerApiApp/App/DiaryService/diaryApi.py

CLAY-zhao/MyLife

994e1f4b2cb20b0bd09edc95ea5ed0b09010a1e3

[ "bzip2-1.0.6" ]

null

from ...settings.config import data from rest_framework.views import APIView from django.http import JsonResponse from .order.diary_serializer import HomeDiarySerializer from ...utils.AppFunctools import modelObject from django.core.paginator import Paginator, EmptyPage, PageNotAnInteger class DiaryApi(APIView): PAGE_COUNT = 6 # 每页显示n条数据 data = { "appStatus": { "errorCode": 0, "errorParameter": "", "message": "操作成功!" }, "content": {} } def get(self, request): """首页获取日记简介的部分内容/首页不展示日记所有内容""" diary_list = modelObject.diary_model.all() paginator = Paginator(diary_list, self.PAGE_COUNT) page = request.GET.get('page', 1) # 获取当前页数,None则取1 try: diary = paginator.page(page) except PageNotAnInteger: # 如果page不为int类型,则返回第1页 diary = paginator.page(self.PAGE_COUNT) except EmptyPage: # 如果page超出获取范围,则返回最后一页 diary = paginator.page(paginator.num_pages) diary_data = HomeDiarySerializer(instance=diary, many=True).data self.data['content']['list'] = diary_data return JsonResponse(self.data)

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0.634568

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1,215

6.333333

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0.264198

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28.255814

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0.206897

0

0.37931

0

null

0

null

0

1

0

8782be8e1dfda01d65552854454e5ce24b5f9726

637

py

Python

.config/polybar/scripts/mail/mail.py

XECortex/dots

ce07f010b2ba80b8105b5bf7786f54df9048ec81

[ "MIT" ]

3

2021-02-18T17:59:17.000Z

2021-02-19T19:54:18.000Z

.config/polybar/scripts/mail/mail.py

XECortex/dots

ce07f010b2ba80b8105b5bf7786f54df9048ec81

[ "MIT" ]

null

.config/polybar/scripts/mail/mail.py

XECortex/dots

ce07f010b2ba80b8105b5bf7786f54df9048ec81

[ "MIT" ]

null

#!/usr/bin/env python import os import imaplib path = os.path.dirname(os.path.realpath(__file__)) mail_client = imaplib.IMAP4_SSL('imap.gmail.com', '993') if not os.path.isfile(f"{path}/config.py"): print(f"⚠ No mail config found. Check out \"{path}/config.py\"") exit() else: from config import * mail_client.login(user, password) def check_mails(): mail_client.select() unread = mail_client.search(None, 'UnSeen') return len(unread[1][0].split()) unread = check_mails() if unread > 0 or not quiet: if not hide_unreads: print(prefix, unread) else: print(prefix) else: print('')

21.233333

68

0.66248

95

637

4.326316

0.568421

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0.058394

0

0.013487

0.185243

637

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21.233333

0.776493

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false

0.045455

0.136364

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0.227273

0.181818

0

null

0

null

0

1

0

8783c7b5deca87958f9de465f6600ca4c5858f14

1,069

py

Python

silence.py

ColesonWelles/SilencePy

7c650d9349dd6a708c94bce9e3b69e58895dc21a

[ "MIT" ]

null

silence.py

ColesonWelles/SilencePy

7c650d9349dd6a708c94bce9e3b69e58895dc21a

[ "MIT" ]

null

silence.py

ColesonWelles/SilencePy

7c650d9349dd6a708c94bce9e3b69e58895dc21a

[ "MIT" ]

null

#!/usr/bin/python ''' sys is needed for argv pilsuc = False for importing pil try tries to import pillow but will install via pip if it fails pillow used for image manipulation open method opens the base image truetype method opens font file if arguments passed to, join method creats text of args else in case no args are passed to draw method creates a draw image object using the base image draws text save method saves image ''' import sys pilsuc = False while pilsuc == False: try: from PIL import Image, ImageDraw, ImageFont pilsuc = True except ImportError: import subprocess subprocess.call([sys.executable, "-m", "pip", "install", "Pillow"]) img = Image.open("img.png") font = ImageFont.truetype("LiberationSans-Regular.ttf", 68) if len(sys.argv) > 1: text = "\n".join(sys.argv[1:]) else: print("Usage: py silence.py [text for meme] - generates \"silence crab\" meme with input text") sys.exit() draw = ImageDraw.Draw(img) draw.text((6,60), text, fill="white", font=font) img.save('export.png')

22.744681

99

0.698784

163

1,069

4.582822

0.558282

0.044177

0.032129

0

0.00814

0.19551

1,069

46

100

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0.860465

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false

0

0.210526

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0.210526

0.052632

0

null

0

null

0

1

0

8783c800b214430bdf292410b4ff190c797fe5e9

271

py

Python

server.py

jackyin68/pyprofiling

3f4180c735b80b978028cad776d30a6b99af1547

[ "Apache-2.0" ]

3

2022-01-10T13:09:45.000Z

2022-02-27T23:26:32.000Z

server.py

jackyin68/pyprofiling

3f4180c735b80b978028cad776d30a6b99af1547

[ "Apache-2.0" ]

null

server.py

jackyin68/pyprofiling

3f4180c735b80b978028cad776d30a6b99af1547

[ "Apache-2.0" ]

1

2022-02-21T15:21:24.000Z

import os import sys path = os.path.dirname(sys.path[0]) if path and path not in sys.path: sys.path.append(path) from flask import Flask app = Flask("Product") @app.route("/") def welcome(): return "欢迎来到通达信数据分析的世界" if __name__ == '__main__': app.run()

13.55

35

0.667897

41

271

4.219512

0.560976

0.16185

0

0.004545

0.188192

271

19

36

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0.110701

0

1

0.083333

false

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0.25

0.083333

0.416667

0

null

0

null

0

1

0

8784d47e997e461bc2117484da1a0b3ef1cc901a

1,995

py

Python

src/predectorutils/subcommands/analysis_tables.py

ccdmb/predector-utils

68e9e72682dd73fff8d1c53969870a2e9628556e

[ "Apache-2.0" ]

null

src/predectorutils/subcommands/analysis_tables.py

ccdmb/predector-utils

68e9e72682dd73fff8d1c53969870a2e9628556e

[ "Apache-2.0" ]

7

2020-06-17T02:37:21.000Z

2021-11-22T02:18:54.000Z

src/predectorutils/subcommands/analysis_tables.py

ccdmb/predector-utils

68e9e72682dd73fff8d1c53969870a2e9628556e

[ "Apache-2.0" ]

null

#!/usr/bin/env python3 import os import argparse from typing import Iterator from typing import Set import sqlite3 import pandas as pd from predectorutils.database import ( load_db, ResultsTable, ResultRow, TargetRow ) def cli(parser: argparse.ArgumentParser) -> None: parser.add_argument( "db", type=str, help="Where to store the database" ) parser.add_argument( "-t", "--template", type=str, default="{analysis}.tsv", help=( "A template for the output filenames. Can use python `.format` " "style variable analysis. Directories will be created." ) ) parser.add_argument( "--mem", type=float, default=1.0, help=( "The amount of RAM in gibibytes to let " "SQLite use for cache." ) ) return def inner( con: sqlite3.Connection, cur: sqlite3.Cursor, args: argparse.Namespace ) -> None: from ..analyses import Analyses tab = ResultsTable(con, cur) targets = list(tab.fetch_targets()) seen: Set[Analyses] = set() for target in targets: if target.analysis in seen: raise ValueError( "There are multiple versions of the same analysis." ) else: seen.add(target.analysis) records = tab.select_target(target, checksums=False) df = pd.DataFrame(map(lambda x: x.as_analysis().as_series(), records)) fname = args.template.format(analysis=str(target.analysis)) dname = os.path.dirname(fname) if dname != '': os.makedirs(dname, exist_ok=True) df.to_csv(fname, sep="\t", index=False, na_rep=".") def runner(args: argparse.Namespace) -> None: try: con, cur = load_db(args.db, args.mem) inner(con, cur, args) except Exception as e: raise e finally: con.commit() con.close() return

21.684783

78

0.582456

234

1,995

4.91453

0.521368

0.023478

0.044348

0.043478

0

0.00436

0.310276

1,995

91

79

21.923077

0.831395

0.010526

0

0.128571

0

0.144957

0

1

0.042857

false

0

0.114286

0

0.185714

0

null

0

null

0

1

0

878755049296809773d0780b76a7eb056449007e

1,740

py

Python

qmmm_neuralnets/files/h5_file_ops.py

adamduster/qmmm_neuralnets

70f35ec0659e8a424cb66ad874d22232c22fcba5

[ "MIT" ]

null

qmmm_neuralnets/files/h5_file_ops.py

adamduster/qmmm_neuralnets

70f35ec0659e8a424cb66ad874d22232c22fcba5

[ "MIT" ]

1

2021-09-17T18:19:48.000Z

qmmm_neuralnets/files/h5_file_ops.py

lin-compchem/qmmm_neuralnets

70f35ec0659e8a424cb66ad874d22232c22fcba5

[ "MIT" ]

null

#!/usr/bin/env python """ Operations over generic quantities of H5 files """ import h5py as h5 import sys def mean_over_files(fnames, key): """ Calculate the mean of of a value over a list of h5 files Parameters ---------- fnames: list of str list of hdf5 files with identical keys key: str Name of key to calculate mean over Returns ------- mean: float or int The mean of the quantity """ num_files = check_file_list(fnames) mean = 0 for fname in fnames: with h5.File(fname, 'r') as ifi: try: mean += ifi[key][:].mean() except ValueError: raise return mean / num_files def check_file_list(fnames): """ Check to see if we have a list of files to do error-checking for various subroutines Parameters ---------- fnames: list of str file names to check if list Returns ------- num_files: int number of files in list """ try: num_files = len(fnames) assert (num_files > 0) except ValueError: sys.stderr.write("Please pass a list of filenames as an argument") raise return num_files def check_for_keys(fname, *keys): """ Check if the key(s) exists in the h5 file Parameters ---------- fname: str The name of the h5 file *keys: keys to check Returns ------- """ with h5.File(fname, 'r') as ifi: all_keys = list(ifi.keys()) for key in keys: if key not in all_keys: sys.stderr.write("Error, key {} not in hdf5 file {}\n".format( key, fname)) raise KeyError

21.75

78

0.551724

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1,740

4.034188

0.333333

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0.022246

0.04661

0.097458

0.044492

0

0.010591

0.348851

1,740

79

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0.822595

0.395977

0

0.285714

0

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0

0.035714

1

0.107143

false

0.035714

0.071429

0

0.25

0

null

0

null

0

1

0

8789b39379bf81890eef79642c63e5f745f80aaf

1,455

py

Python

exercicios/exercicio096.py

Helton-Rubens/Python-3

eb6d5ee71bcb2a2a80de4eaea942bd0c41d846b7

[ "MIT" ]

null

exercicios/exercicio096.py

Helton-Rubens/Python-3

eb6d5ee71bcb2a2a80de4eaea942bd0c41d846b7

[ "MIT" ]

null

exercicios/exercicio096.py

Helton-Rubens/Python-3

eb6d5ee71bcb2a2a80de4eaea942bd0c41d846b7

[ "MIT" ]

null

jogador = dict() jogadores = list() dec = ' ' while dec not in 'Nn': jogador['nome'] = str(input('Qual o nome do jogador? ')).strip().title() i = int(input(f'Quantas partidas {jogador["nome"]} jogou? ')) gols = [] for i in range(1, i+1): gols.append(int(input(f'Quantos gols {jogador["nome"]} fez no {i}º jogo? '))) jogador['gols'] = gols[:] jogador['total'] = sum(gols) jogadores.append(jogador.copy()) dec = str(input('Quer continuar?[S/N] '))[0] while dec[0] not in 'SsNn': print('Erro! Digite apenas "Sim" ou "Não"') dec = str(input('Quer continuar?[S/N] '))[0] print('-='*40) print(f'{"Cod":>3} {"Nome":<15}{"Gols":<15}{"Total":<15}') print('-='*40) for n, i in enumerate(jogadores): print(f'{n:>3}', end=' ') for u in i.values(): print(f'{str(u):<15}', end='') print() print() while True: print('--'*30) busca = int(input('Deseja buscar os dados de qual jogador?(999 para parar) ')) while busca > len(jogadores) or busca < 0: print(f'Inválido. Não existe jogador com código {busca}!') print('--'*30) busca = int(input('Deseja buscar os dados de qual jogador?(999 para parar)')) if busca == 999: break else: print(f'levantamento do jogador {jogadores[busca]["nome"]}') for i, c in enumerate(jogadores[busca]['gols']): print(f' No {i+1}º jogo, {jogadores[busca]["nome"]}, fez {c} gols.')

37.307692

85

0.569072

212

1,455

3.90566

0.367925

0.043478

0.021739

0.036232

0.219807

0.154589

0

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0.221306

1,455

38

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0.38488

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false

0

0.342105

0

null

0

null

0

1

0

878aef6bb4057ee6f517c4833149a1d1ed15902f

25,168

py

Python

amical/data_processing.py

tomasstolker/AMICAL

c9bbf8e4a468313efff3b349fffea7648c411a51

[ "MIT" ]

null

amical/data_processing.py

tomasstolker/AMICAL

c9bbf8e4a468313efff3b349fffea7648c411a51

[ "MIT" ]

null

amical/data_processing.py

tomasstolker/AMICAL

c9bbf8e4a468313efff3b349fffea7648c411a51

[ "MIT" ]

null

""" @author: Anthony Soulain (University of Sydney) ------------------------------------------------------------------------- AMICAL: Aperture Masking Interferometry Calibration and Analysis Library ------------------------------------------------------------------------- Function related to data cleaning (ghost, background correction, centering, etc.) and data selection (sigma-clipping, centered flux,). -------------------------------------------------------------------- """ import sys import warnings import numpy as np from astropy.convolution import Gaussian2DKernel from astropy.convolution import interpolate_replace_nans from astropy.io import fits from matplotlib import pyplot as plt from matplotlib.colors import PowerNorm from termcolor import cprint from tqdm import tqdm from amical.tools import apply_windowing from amical.tools import crop_max from amical.tools import find_max def _apply_patch_ghost(cube, xc, yc, radius=20, dx=0, dy=-200, method="bg"): """Apply a patch on an eventual artifacts/ghosts on the spectral filter (i.e. K1 filter of SPHERE presents an artifact/ghost at (392, 360)). Arguments: ---------- `cube` {array} -- Data cube,\n `xc` {int} -- x-axis position of the artifact,\n `yc` {int} -- y-axis position of the artifact. Keyword Arguments: ---------- `radius` {int} -- Radius to apply the patch in a circle (default: {10}),\n `dy` {int} -- Offset pixel number to compute background values (default: {0}),\n `dx` {int} -- Same along y-axis (default: {0}),\n `method` {str} -- If 'bg', the replacement values are the background computed at xc+dx, yx+dy, else zero is apply (default: {'bg'}). """ cube_corrected = [] for i in range(len(cube)): imA = cube[i].copy() isz = imA.shape[0] xc_off, yc_off = xc + dx, yc + dy xx, yy = np.arange(isz), np.arange(isz) xx_c = xx - xc yy_c = yc - yy xx_off = xx - xc_off yy_off = yc_off - yy distance = np.sqrt(xx_c**2 + yy_c[:, np.newaxis] ** 2) distance_off = np.sqrt(xx_off**2 + yy_off[:, np.newaxis] ** 2) cond_patch = distance <= radius cond_bg = distance_off <= radius if method == "bg": imA[cond_patch] = np.mean(imA[cond_bg]) elif method == "zero": imA[cond_patch] = 0 cube_corrected.append(imA) cube_corrected = np.array(cube_corrected) return cube_corrected def select_data(cube, clip_fact=0.5, clip=False, verbose=True, display=True): """Check the cleaned data cube using the position of the maximum in the fft image (supposed to be zero). If not in zero position, the fram is rejected. It can apply a sigma-clipping to select only the frames with the highest total fluxes. Parameters: ----------- `cube` {array} -- Data cube,\n `clip_fact` {float} -- Relative sigma if rejecting frames by sigma-clipping (default=False),\n `clip` {bool} -- If True, sigma-clipping is used,\n `verbose` {bool} -- If True, print informations in the terminal,\n `display` {bool} -- If True, plot figures. """ fft_fram = abs(np.fft.fft2(cube)) # flag_fram, cube_flagged, cube_cleaned_checked = [], [], [] fluxes, flag_fram, good_fram = [], [], [] for i in range(len(fft_fram)): fluxes.append(fft_fram[i][0, 0]) pos_max = np.argmax(fft_fram[i]) if pos_max != 0: flag_fram.append(i) else: good_fram.append(cube[i]) fluxes = np.array(fluxes) flag_fram = np.array(flag_fram) best_fr = np.argmax(fluxes) worst_fr = np.argmin(fluxes) std_flux = np.std(fluxes) med_flux = np.median(fluxes) if verbose: if (med_flux / std_flux) <= 5.0: cprint( "\nStd of the fluxes along the cube < 5 (%2.1f):\n -> sigma clipping is suggested (clip=True)." % (med_flux / std_flux), "cyan", ) limit_flux = med_flux - clip_fact * std_flux if clip: cond_clip = fluxes > limit_flux cube_cleaned_checked = cube[cond_clip] ind_clip = np.where(fluxes <= limit_flux)[0] else: ind_clip = [] cube_cleaned_checked = np.array(good_fram) ind_clip2 = np.where(fluxes <= limit_flux)[0] if ((worst_fr in ind_clip2) and clip) or (worst_fr in flag_fram): ext = "(rejected)" else: ext = "" diffmm = 100 * abs(np.max(fluxes) - np.min(fluxes)) / med_flux if display: plt.figure(figsize=(10, 5)) plt.plot( fluxes, label=r"|$\Delta F$|/$\sigma_F$=%2.0f (%2.2f %%)" % (med_flux / std_flux, diffmm), lw=1, ) if len(flag_fram) > 0: plt.scatter( flag_fram, fluxes[flag_fram], s=52, facecolors="none", edgecolors="r", label="Rejected frames (maximum fluxes)", ) if clip: if len(ind_clip) > 0: plt.plot( ind_clip, fluxes[ind_clip], "x", color="crimson", label="Rejected frames (clipping)", ) else: print("0") # plt.hlines(limit_flux, 0, len(fluxes), ) plt.axhline( limit_flux, lw=3, color="#00b08b", ls="--", label="Clipping threshold", zorder=10, ) plt.legend(loc="best", fontsize=9) plt.ylabel("Flux [counts]") plt.xlabel("# frames") plt.grid(alpha=0.2) plt.tight_layout() plt.figure(figsize=(7, 7)) plt.subplot(2, 2, 1) plt.title("Best fram (%i)" % best_fr) plt.imshow(cube[best_fr], norm=PowerNorm(0.5, vmin=0), cmap="afmhot") plt.subplot(2, 2, 2) plt.imshow(np.fft.fftshift(fft_fram[best_fr]), cmap="gist_stern") plt.subplot(2, 2, 3) plt.title("Worst fram (%i) %s" % (worst_fr, ext)) plt.imshow(cube[worst_fr], norm=PowerNorm(0.5, vmin=0), cmap="afmhot") plt.subplot(2, 2, 4) plt.imshow(np.fft.fftshift(fft_fram[worst_fr]), cmap="gist_stern") plt.tight_layout() plt.show(block=False) if verbose: n_good = len(cube_cleaned_checked) n_bad = len(cube) - n_good if clip: cprint("\n---- σ-clip + centered fluxes selection ---", "cyan") else: cprint("\n---- centered fluxes selection ---", "cyan") print( "%i/%i (%2.1f%%) are flagged as bad frames" % (n_bad, len(cube), 100 * float(n_bad) / len(cube)) ) return cube_cleaned_checked def _get_ring_mask(r1, dr, isz, center=None): if center is None: xc, yc = isz // 2, isz // 2 else: xc, yc = center xx, yy = np.arange(isz), np.arange(isz) xx2 = xx - xc yy2 = yc - yy distance = np.sqrt(xx2**2 + yy2[:, np.newaxis] ** 2) inner_cond = r1 <= distance if dr is not None: r2 = r1 + dr outer_cond = distance <= r2 else: outer_cond = True cond_bg = inner_cond & outer_cond if dr is not None and np.all(outer_cond): warnings.warn( "The outer radius is out of the image, using everything beyond r1 as background", RuntimeWarning, ) return cond_bg def sky_correction(imA, r1=None, dr=None, verbose=False, *, center=None, mask=None): """ Perform background sky correction to be as close to zero as possible. This requires either a radius (r1) to define the background boundary, optionally with a ring width dr, or a boolean mask with the same shape as the image. """ # FUTURE: Future AMICAL release should raise error if r1 is None and mask is None: warnings.warn( "The default value of r1 and dr is now None. Either mask or r1 must be set" " explicitely. In the future, this will result in an error." " Setting r1=100 and dr=20", PendingDeprecationWarning, ) r1 = 100 dr = 20 if r1 is not None and mask is not None: raise TypeError("Only one of mask and r1 can be specified") elif r1 is None and dr is not None: raise TypeError("dr cannot be set when r1 is None") elif r1 is not None: isz = imA.shape[0] cond_bg = _get_ring_mask(r1, dr, isz, center=center) elif mask is not None: if mask.shape != imA.shape: raise ValueError("mask should have the same shape as image") elif not mask.any(): warnings.warn( "Background not computed because mask has no True values", RuntimeWarning, ) cond_bg = mask do_bg = cond_bg.any() if do_bg: try: minA = imA.min() imB = imA + 1.01 * abs(minA) backgroundB = np.mean(imB[cond_bg]) imC = imB - backgroundB backgroundC = np.mean(imC[cond_bg]) except IndexError: do_bg = False # Not using else because do_bg can change in except above if not do_bg: imC = imA.copy() backgroundC = 0 warnings.warn( "Background not computed, likely because specified radius is out of bounds", RuntimeWarning, ) elif verbose: print( f"Sky correction of {backgroundB} was subtracted," f" remaining background is {backgroundC}." ) return imC, backgroundC def fix_bad_pixels(image, bad_map, add_bad=None, x_stddev=1): """Replace bad pixels with values interpolated from their neighbors (interpolation is made with a gaussian kernel convolution).""" if add_bad is None: add_bad = [] if len(add_bad) != 0: bad_map = bad_map.copy() # Don't modify input bad pixel map, use a copy for j in range(len(add_bad)): bad_map[add_bad[j][1], add_bad[j][0]] = 1 img_nan = image.copy() img_nan[bad_map == 1] = np.nan kernel = Gaussian2DKernel(x_stddev=x_stddev) fixed_image = interpolate_replace_nans(img_nan, kernel) return fixed_image def _get_3d_bad_pixels(bad_map, add_bad, data): """ Format 3d bad pixel cube from arbitrary bad pixel input Parameters ---------- `bad_map` {np.ndarray}: Bad pixel map in 2d or 3d (can also be None)\n `add_bad` {list}: list of bad pixel coordinates\n `data` {np.ndarray}: Array with the data corresponding to the bad pixel map\n Returns: -------- `bad_map` {np.array}: 3d bad map with same shape as data cube `add_bad` {list}: add_bad list compatible with 3d dataset """ n_im = data.shape[0] # Add check to create default add_bad list (not use mutable data) if add_bad is None or len(add_bad) == 0: # Reshape add_bad to simplify indexing in loop add_bad = [ [], ] * n_im else: add_bad = np.array(add_bad) if add_bad.ndim == 2 and len(add_bad[0]) != 0: add_bad = np.repeat(add_bad[np.newaxis, :], n_im, axis=0) elif add_bad.ndim == 3: if add_bad.shape[0] != n_im: raise ValueError("3D add_bad should have one list per frame") if (bad_map is None) and (len(add_bad) != 0): # If we have extra bad pixels, define bad_map with same shape as image bad_map = np.zeros_like(data, dtype=bool) elif bad_map is not None: # Shape should match data if bad_map.ndim == 2 and bad_map.shape != data[0].shape: raise ValueError( f"2D bad_map should have the same shape as a frame ({data[0].shape})," f" but has shape {bad_map.shape}" ) elif bad_map.ndim == 3 and bad_map.shape != data.shape: raise ValueError( f"3D bad_map should have the same shape as data cube ({data.shape})," f" but has shape {bad_map.shape}" ) elif bad_map.ndim == 2: bad_map = np.repeat(bad_map[np.newaxis, :], n_im, axis=0) return bad_map, add_bad def show_clean_params( filename, isz, r1=None, dr=None, bad_map=None, add_bad=None, edge=0, remove_bad=True, nframe=0, ihdu=0, f_kernel=3, offx=0, offy=0, apod=False, window=None, *, mask=None, ): """Display the input parameters for the cleaning. Parameters: ----------- `filename` {str}: filename containing the datacube,\n `isz` {int}: Size of the cropped image (default: 256)\n `r1` {int}: Radius of the rings to compute background sky (default: 100)\n `dr` {int}: Outer radius to compute sky (default: 10)\n `bad_map` {array}: Bad pixel map with 0 and 1 where 1 set for a bad pixel (default: None),\n `add_bad` {list}: List of 2d coordinates of bad pixels/cosmic rays (default: []),\n `edge` {int}: Number of pixel to be removed on the edge of the image (SPHERE),\n `remove_bad` {bool}: If True, the bad pixels are removed using a gaussian interpolation,\n `nframe` {int}: Frame number to be shown (default: 0),\n `ihdu` {int}: Hdu number of the fits file. Normally 1 for NIRISS and 0 for SPHERE (default: 0). """ with fits.open(filename) as fd: data = fd[ihdu].data img0 = data[nframe] dims = img0.shape if isz is None: print( "Warning: isz not found (None by default). isz is set to the original image size (%i)" % (dims[0]), file=sys.stderr, ) isz = dims[0] bad_map, add_bad = _get_3d_bad_pixels(bad_map, add_bad, data) bmap0 = bad_map[nframe] ab0 = add_bad[nframe] if edge != 0: img0[:, 0:edge] = 0 img0[:, -edge:-1] = 0 img0[0:edge, :] = 0 img0[-edge:-1, :] = 0 if (bad_map is not None) & (remove_bad): img1 = fix_bad_pixels(img0, bmap0, add_bad=ab0) else: img1 = img0.copy() cropped_infos = crop_max(img1, isz, offx=offx, offy=offy, f=f_kernel) pos = cropped_infos[1] noBadPixel = False bad_pix_x, bad_pix_y = [], [] if np.any(bmap0): if len(ab0) != 0: for j in range(len(ab0)): bmap0[ab0[j][1], ab0[j][0]] = 1 bad_pix = np.where(bmap0 == 1) bad_pix_x = bad_pix[0] bad_pix_y = bad_pix[1] else: noBadPixel = True theta = np.linspace(0, 2 * np.pi, 100) x0 = pos[0] y0 = pos[1] if r1 is not None: x1 = r1 * np.cos(theta) + x0 y1 = r1 * np.sin(theta) + y0 if dr is not None: r2 = r1 + dr x2 = r2 * np.cos(theta) + x0 y2 = r2 * np.sin(theta) + y0 sky_method = "ring" elif mask is not None: bg_coords = np.where(mask == 1) bg_x = bg_coords[0] bg_y = bg_coords[1] sky_method = "mask" if window is not None: r3 = window x3 = r3 * np.cos(theta) + x0 y3 = r3 * np.sin(theta) + y0 xs1, ys1 = x0 + isz // 2, y0 + isz // 2 xs2, ys2 = x0 - isz // 2, y0 + isz // 2 xs3, ys3 = x0 - isz // 2, y0 - isz // 2 xs4, ys4 = x0 + isz // 2, y0 - isz // 2 max_val = img1[y0, x0] fig = plt.figure(figsize=(5, 5)) plt.title("--- CLEANING PARAMETERS ---") plt.imshow(img1, norm=PowerNorm(0.5, vmin=0, vmax=max_val), cmap="afmhot") if sky_method == "ring": if dr is not None: plt.plot(x1, y1, label="Inner radius for sky subtraction") plt.plot(x2, y2, label="Outer radius for sky subtraction") else: plt.plot(x1, y1, label="Boundary for sky subtraction") elif sky_method == "mask": plt.scatter( bg_y, bg_x, color="None", marker="s", edgecolors="C0", s=20, label="Pixels used for sky subtraction", ) if apod: if window is not None: plt.plot(x3, y3, "--", label="Super-gaussian windowing") plt.plot(x0, y0, "+", color="c", ms=10, label="Centering position") plt.plot( [xs1, xs2, xs3, xs4, xs1], [ys1, ys2, ys3, ys4, ys1], "w--", label="Resized image", ) plt.xlim((0, dims[0] - 1)) plt.ylim((0, dims[1] - 1)) if not noBadPixel: if remove_bad: label = "Fixed hot/bad pixels" else: label = "Hot/bad pixels" plt.scatter( bad_pix_y, bad_pix_x, color="None", marker="s", edgecolors="r", facecolors="None", s=20, label=label, ) plt.xlabel("X [pix]") plt.ylabel("Y [pix]") plt.legend(fontsize=8, loc=1) plt.tight_layout() return fig def _apply_edge_correction(img0, edge=0): """Remove the bright edges (set to 0) observed for some detectors (SPHERE).""" if edge != 0: img0[:, 0:edge] = 0 img0[:, -edge:-1] = 0 img0[0:edge, :] = 0 img0[-edge:-1, :] = 0 return img0 def _remove_dark(img1, darkfile=None, ihdu=0, verbose=False): if darkfile is not None: with fits.open(darkfile) as hdu: dark = hdu[ihdu].data if verbose: print("Dark cube shape is:", dark.shape) master_dark = np.mean(dark, axis=0) img1 -= master_dark return img1 def clean_data( data, isz=None, r1=None, dr=None, edge=0, bad_map=None, add_bad=None, apod=True, offx=0, offy=0, sky=True, window=None, darkfile=None, f_kernel=3, verbose=False, *, mask=None, ): """Clean data. Parameters: ----------- `data` {np.array} -- datacube containing the NRM data\n `isz` {int} -- Size of the cropped image (default: {None})\n `r1` {int} -- Radius of the rings to compute background sky (default: {None})\n `dr` {int} -- Outer radius to compute sky (default: {None})\n `edge` {int} -- Patch the edges of the image (VLT/SPHERE artifact, default: {200}),\n `checkrad` {bool} -- If True, check the resizing and sky substraction parameters (default: {False})\n Returns: -------- `cube` {np.array} -- Cleaned datacube. """ n_im = data.shape[0] cube_cleaned = [] # np.zeros([n_im, isz, isz]) l_bad_frame = [] bad_map, add_bad = _get_3d_bad_pixels(bad_map, add_bad, data) for i in tqdm(range(n_im), ncols=100, desc="Cleaning", leave=False): img0 = data[i] img0 = _apply_edge_correction(img0, edge=edge) if bad_map is not None: img1 = fix_bad_pixels(img0, bad_map[i], add_bad=add_bad[i]) else: img1 = img0.copy() img1 = _remove_dark(img1, darkfile=darkfile, verbose=verbose) if isz is not None: # Get expected center for sky correction filtmed = f_kernel is not None center = find_max(img1, filtmed=filtmed, f=f_kernel) else: center = None if sky and (r1 is not None or mask is not None): img_biased = sky_correction( img1, r1=r1, dr=dr, verbose=verbose, center=center, mask=mask )[0] elif sky: warnings.warn( "sky is set to True, but r1 and mask are set to None. Skipping sky correction", RuntimeWarning, ) img_biased = img1.copy() else: img_biased = img1.copy() img_biased[img_biased < 0] = 0 # Remove negative pixels if isz is not None: # Get expected center for sky correction filtmed = f_kernel is not None im_rec_max = crop_max( img_biased, isz, offx=offx, offy=offy, filtmed=filtmed, f=f_kernel )[0] else: im_rec_max = img_biased.copy() if ( (im_rec_max.shape[0] != im_rec_max.shape[1]) or (isz is not None and im_rec_max.shape[0] != isz) or (isz is None and im_rec_max.shape[0] != img0.shape[0]) ): l_bad_frame.append(i) else: if apod and window is not None: img = apply_windowing(im_rec_max, window=window) elif apod: warnings.warn( "apod is set to True, but window is None. Skipping apodisation", RuntimeWarning, ) img = im_rec_max.copy() else: img = im_rec_max.copy() cube_cleaned.append(img) if verbose: print("Bad centering frame number:", l_bad_frame) cube_cleaned = np.array(cube_cleaned) return cube_cleaned def select_clean_data( filename, isz=256, r1=None, dr=None, edge=0, clip=True, bad_map=None, add_bad=None, offx=0, offy=0, clip_fact=0.5, apod=True, sky=True, window=None, darkfile=None, f_kernel=3, verbose=False, ihdu=0, display=False, *, remove_bad=True, nframe=0, mask=None, ): """Clean and select good datacube (sigma-clipping using fluxes variations). Parameters: ----------- `filename` {str}: filename containing the datacube,\n `isz` {int}: Size of the cropped image (default: {256})\n `r1` {int}: Radius of the rings to compute background sky (default: {100})\n `dr` {int}: Outer radius to compute sky (default: {10})\n `edge` {int}: Patch the edges of the image (VLT/SPHERE artifact, default: {0}),\n `clip` {bool}: If True, sigma-clipping is used to reject frames with low integrated flux,\n `clip_fact` {float}: Relative sigma if rejecting frames by sigma-clipping,\n `apod` {bool}: If True, apodisation is performed in the image plan using a super-gaussian function (known as windowing). The gaussian FWHM is set by the parameter `window`,\n `window` {float}: FWHM of the super-gaussian to apodise the image (smoothly go to zero on the edges),\n `sky` {bool}: If True, the sky is remove using the annulus technique (computed between `r1` and `r1` + `dr`), `darkfile` {str}: If specified (default: None), the input dark (master_dark averaged if multiple integrations) is substracted from the raw image,\n image,\n `f_kernel` {float}: kernel size used in the applied median filter (to find the center). `remove_bad` {bool}: If True, the bad pixels are removed in the cleaning parameter plots using a gaussian interpolation (default: {True}),\n `nframe` {int}: Frame number used to show cleaning parameters (default: {0}),\n Returns: -------- `cube_final` {np.array}: Cleaned and selected datacube. """ with fits.open(filename) as hdu: cube = hdu[ihdu].data hdr = hdu[0].header ins = hdr.get("INSTRUME", None) if ins == "SPHERE": seeing_start = float(hdr["HIERARCH ESO TEL AMBI FWHM START"]) seeing = float(hdr["HIERARCH ESO TEL IA FWHM"]) seeing_end = float(hdr["HIERARCH ESO TEL AMBI FWHM END"]) if verbose: print("\n----- Seeing conditions -----") print( "%2.2f (start), %2.2f (end), %2.2f (Corrected AirMass)" % (seeing_start, seeing_end, seeing) ) # Add check to create default add_bad list (not use mutable data) if add_bad is None: add_bad = [] if r1 is None and mask is None and sky: warnings.warn( "The default value of r1 is now None. Either r1 or mask should be set explicitely. This will raise an error in the future.", PendingDeprecationWarning, ) r1 = 100 if dr is None: dr = 10 elif r1 is not None and dr is None and mask is None and sky: warnings.warn( "The default value of dr is now None. dr must be set explicitely to be used.", PendingDeprecationWarning, ) dr = 10 if display: show_clean_params( filename, isz, r1, dr, bad_map=bad_map, add_bad=add_bad, edge=edge, remove_bad=remove_bad, nframe=nframe, ihdu=ihdu, f_kernel=f_kernel, offx=offx, offy=offy, apod=apod, window=window, ) cube_cleaned = clean_data( cube, isz=isz, r1=r1, edge=edge, bad_map=bad_map, add_bad=add_bad, dr=dr, sky=sky, apod=apod, window=window, f_kernel=f_kernel, offx=offx, offy=offy, darkfile=darkfile, verbose=verbose, mask=mask, ) if cube_cleaned is None: return None cube_final = select_data( cube_cleaned, clip=clip, clip_fact=clip_fact, verbose=verbose, display=display ) return cube_final

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136

0.562738

3,488

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3.948681

0.148796

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0.01699

0.008713

0.276919

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0

1

0.019231

false

0

0.022727

0

0.062937

0.020979

0

null

0

null

0

1

0

878fec7820fb97d9b9711929385ca95004c7e7c9

3,547

py

Python

saltpad/core.py

novapost/saltpad

863e79e6eef7f36d16050170f26203854208283a

[ "Apache-2.0" ]

1

2016-01-08T20:56:20.000Z

saltpad/core.py

novapost/saltpad

863e79e6eef7f36d16050170f26203854208283a

[ "Apache-2.0" ]

null

saltpad/core.py

novapost/saltpad

863e79e6eef7f36d16050170f26203854208283a

[ "Apache-2.0" ]

null

import os import sys import salt.config import salt.client import salt.runner import salt.key import pymongo from salt.output import highstate from functools import wraps def mproperty(fn): attribute = "_memo_%s" % fn.__name__ @property @wraps(fn) def _property(self): if not hasattr(self, attribute): setattr(self, attribute, fn(self)) return getattr(self, attribute) return _property class SaltStackClient(object): def __init__(self, collection_name="saltpad"): master_opts = salt.config.master_config( os.environ.get('SALT_MASTER_CONFIG', '/etc/salt/master')) if not 'color' in master_opts: master_opts['color'] = True # Inject master_opts highstate.__opts__ = master_opts minion_opts = salt.config.client_config( os.environ.get('SALT_MINION_CONFIG', '/etc/salt/minion')) self.local = salt.client.LocalClient() self.runner = salt.runner.RunnerClient(master_opts) self.key = salt.key.Key(master_opts) self.collection_name = collection_name self.con = pymongo.MongoClient() self.db = self.con[self.collection_name] self._minions = None self.highstate_cache = {} @property def minions(self): if self._minions is None: minions = self.local.cmd('*', 'test.ping', timeout=0) keys = self.key.list_keys() ret = {} ret['up'] = sorted(minions) ret['down'] = sorted(set(keys['minions']) - set(minions)) self._minions = ret return self._minions def get_minion_status(self, minion_name): if minion_name in self.minions["up"]: return "up" elif minion_name in self.minions["down"]: return "down" else: return "Bad minion_name" def _reload_roles(self): self._minions_roles = {} self._roles_minions = {} for minion in self.minions["up"]: roles = self.local.cmd(minion, 'grains.get', ['roles'])[minion] self._minions_roles[minion] = roles for role in roles: self._roles_minions.setdefault(role, []).append(minion) def minions_roles(self): self._reload_roles() return self._minions_roles def roles_minions(self): self._reload_roles() return self._roles_minions def get_job_id(self, minion, jid): return self.con[minion].find_one({'jid': jid}) def get_multiple_job_status(self, minion, key=None, max=5): query = {} if key: query['key'] = key return list(self.db[minion].find(query).sort('_id', -1).limit(max)) def get_job_status(self, minion, jid, key=None): query = {'jid': jid} if key: query['key'] = key return self.db[minion].find_one(query) def run_job(self, minion, fun, key=None, *args, **kwargs): result = self.local.run_job(minion, fun, timeout=99999999999999, ret='nova_mongo_return', arg=args, kwarg=kwargs) if key is None: key = fun self.db[minion].insert({'jid': result['jid'], 'key': key}) return result['jid'] def cmd(self, target, fun, timeout=None, *args, **kwargs): return self.local.cmd(target, fun, arg=args, timeout=timeout, kwarg=kwargs) def cmd_iter(self, target, fun, *args, **kwargs): return self.local.cmd_iter(target, fun, arg=args, kwarg=kwargs)

29.07377

84

0.605018

441

3,547

4.680272

0.23356

0.053295

0.023256

0.017442

0.120155

0.07655

0

0.006584

0.272061

3,547

121

85

29.31405

0.792796

0.005075

0

0.088889

0

0.05784

0

1

0.155556

false

0

0.1

0.033333

0.422222

0

null

0

null

0

1

0

8793d15bb2a0283134868ae0228a8a20d4336c28

2,633

py

Python

source_pytorch/predict.py

koljamaier/cnn-classifier

176cb5992ac90564d2ce74cfebda7bb0827edbb8

[ "MIT" ]

null

source_pytorch/predict.py

koljamaier/cnn-classifier

176cb5992ac90564d2ce74cfebda7bb0827edbb8

[ "MIT" ]

9

2021-03-19T02:33:43.000Z

2022-03-11T23:55:09.000Z

source_pytorch_style_transfer/predict.py

koljamaier/cnn-classifier

176cb5992ac90564d2ce74cfebda7bb0827edbb8

[ "MIT" ]

null

# import libraries import os import numpy as np import torch from six import BytesIO from torchvision import datasets, models, transforms import torch.nn as nn # default content type is numpy array NP_CONTENT_TYPE = 'application/x-npy' def model_fn(model_dir): """Load the PyTorch model from the `model_dir` directory.""" print("Loading model.") device = torch.device("cuda" if torch.cuda.is_available() else "cpu") model = models.vgg16(pretrained=True) # models.resnet50(pretrained=True) for param in model.parameters(): param.requires_grad = False # vgg16 n_inputs = model.classifier[6].in_features last_layer = nn.Linear(n_inputs, 133) model.classifier[6] = last_layer # Load the stored model parameters. model_path = os.path.join(model_dir, 'model.pth') with open(model_path, 'rb') as f: model.load_state_dict(torch.load(f)) # set to eval mode, could use no_grad model.to(device).eval() print("Done loading model.") return model # Provided input data loading def input_fn(serialized_input_data, content_type): """ We assume, that data will be passed in as numpy array. With this information we can deserialize the data straightforward """ device = torch.device("cuda" if torch.cuda.is_available() else "cpu") print('Deserializing the input data.') if content_type == NP_CONTENT_TYPE: stream = BytesIO(serialized_input_data) return np.load(stream) raise Exception('Requested unsupported ContentType in content_type: ' + content_type) # Provided output data handling def output_fn(prediction_output, accept): print('Serializing the generated output.') if accept == NP_CONTENT_TYPE: stream = BytesIO() np.save(stream, prediction_output) return stream.getvalue(), accept raise Exception('Requested unsupported ContentType in Accept: ' + accept) # this function gets called after our model is deployed. It gives back the prediction label for the dog breed def predict_fn(input_data, model): print('Predicting class labels for the input data...') device = torch.device("cuda" if torch.cuda.is_available() else "cpu") data = torch.from_numpy(input_data.astype('float32')) data = data.to(device) # Put the model into evaluation mode model.eval() # Compute the result of applying the model to the input data # The variable `out_label` should be a rounded value between 0 and 133 (our dog breed classes) out = model(data) out_np = out.cpu().detach().numpy() out_label = out_np.round() return out_label

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null

0

null

0

1

0

8793efeb9b152a5d597ac63b008fb86ba1a50fa4

2,799

py

Python

tasky/tasks/queue.py

jreese/tasky

681f4e5a9a60a0eb838b89f320309cfb45a56242

[ "MIT" ]

10

2016-09-24T12:08:13.000Z

2022-03-25T12:53:45.000Z

tasky/tasks/queue.py

jreese/tasky

681f4e5a9a60a0eb838b89f320309cfb45a56242

[ "MIT" ]

null

tasky/tasks/queue.py

jreese/tasky

681f4e5a9a60a0eb838b89f320309cfb45a56242

[ "MIT" ]

null

# Copyright 2016 John Reese # Licensed under the MIT license import asyncio import logging from concurrent.futures import CancelledError from typing import Any from .task import Task Log = logging.getLogger('tasky.tasks') class QueueTask(Task): '''Run a method on the asyncio event loop for each item inserted into this task's work queue. Can use multiple "workers" to process the work queue. Failed work items (those generating exceptions) will be dropped -- workers must manually requeue any work items that need to be reprocessed.''' WORKERS = 1 MAXSIZE = 0 QUEUE = None OPEN = True def __init__(self, id: int=0): '''Initialize the shared work queue for all workers.''' super().__init__() if self.__class__.QUEUE is None: self.__class__.QUEUE = asyncio.Queue(self.MAXSIZE) self.id = max(0, id) @property def name(self): return '{0}({1})'.format(self.__class__.__name__, self.id) @classmethod def close(cls): '''Mark the queue as being "closed". Once closed, workers will stop running once the work queue becomes empty.''' Log.debug('closing %s work queue', cls.__name__) cls.OPEN = False async def init(self) -> None: if self.id == 0: Log.debug('initializing %s', self.name) for task_id in range(1, self.WORKERS): task = self.__class__(id=task_id) Log.debug('spawning %s', task.name) await self.tasky.insert(task) async def run(self, item: Any) -> None: '''Override this method to define what happens when your task runs.''' await self.sleep(1.0) async def run_task(self) -> None: '''Initialize the queue and spawn extra worker tasks if this if the first task. Then wait for work items to enter the task queue, and execute the `run()` method with the current work item.''' while self.running: try: item = self.QUEUE.get_nowait() Log.debug('%s processing work item', self.name) await self.run(item) Log.debug('%s completed work item', self.name) self.QUEUE.task_done() except asyncio.QueueEmpty: if self.OPEN: await self.sleep(0.05) else: Log.debug('%s queue closed and empty, stopping', self.name) return except CancelledError: Log.debug('%s cancelled, dropping work item') self.QUEUE.task_done() raise except Exception: Log.exception('%s failed work item', self.name) self.QUEUE.task_done()

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0.283019

0

null

0

null

0

1

0

87954da61956d763541972b56cae90b64dbf3274

2,076

py

Python

codenames/routes.py

Schluggi/codenames

094f3621ad17597fede8d6438f41e60dbb1f7057

[ "MIT" ]

3

2020-12-13T18:16:06.000Z

2021-04-13T09:25:23.000Z

codenames/routes.py

Schluggi/codenames

094f3621ad17597fede8d6438f41e60dbb1f7057

[ "MIT" ]

12

2020-06-17T18:23:05.000Z

2022-03-12T00:52:43.000Z

codenames/routes.py

Schluggi/codenames

094f3621ad17597fede8d6438f41e60dbb1f7057

[ "MIT" ]

null

from flask import render_template, redirect, url_for, flash, make_response, session, json from . import app, models, helper, websocket from .forms import IndexForm, GameForm @app.route('/', methods=['GET', 'POST']) def index(): form = IndexForm() if form.validate_on_submit(): #: check if the game already exists if not models.Game.query.filter_by(name=form.game_name.data).first(): #: create a new game helper.new_game(form.game_name.data, form.game_mode.data) flash('New game created', category='success') return redirect(url_for('games', game_name=form.game_name.data)) return render_template('index.html', form=form) @app.route('/g/<game_name>', methods=['GET', 'POST']) @app.route('/g/') def games(game_name=None): form = GameForm() game = models.Game.query.filter_by(name=game_name).first() if not game: flash('Game not found', category='error') return redirect(url_for('index')) session['game_id'] = game.id if form.validate_on_submit(): #: start a new round if form.game_mode.data: game_mode = form.game_mode.data else: game_mode = game.mode helper.new_game(game_name, game_mode, new_round=True) flash('New round started', category='success') #: all clients have to reload the website websocket.reload(game.id) #: get the field image chunks from database image_chunks = json.loads(game.images) return render_template('game.html', rows=image_chunks, game=game, form=form, game_modes=app.game_modes) @app.route('/static/js/game.js') def game(): resp = make_response(render_template('js/game.js')) resp.headers['Content-type'] = 'text/javascript;charset=UTF-8' return resp @app.errorhandler(500) def error_500(_): flash('Game error occurred (500)', category='error') return redirect(url_for('index')) @app.errorhandler(404) def error_404(_): flash('Page not found (404)', category='error') return redirect(url_for('index'))

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2,076

4.631944

0.322917

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0.052474

0.05997

0.185907

0.125937

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0

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0.197495

2,076

68

108

30.529412

0.789316

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0

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0

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0.015128

0

1

0.113636

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0

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0.340909

0

null

0

null

0

1

0

87962fb6b6502f3e6b62209e2b99cbed8a8f548f

1,976

py

Python

Homework 1/question_solutions/question_3_trunc_error.py

rukmal/FE-621-Homework

9c7cef7931b58aed54867acd8e8cf1928bc6d2dd

[ "MIT" ]

4

2020-04-29T04:34:50.000Z

2021-11-11T07:49:08.000Z

Homework 1/question_solutions/question_3_trunc_error.py

rukmal/FE-621-Homework

9c7cef7931b58aed54867acd8e8cf1928bc6d2dd

[ "MIT" ]

null

Homework 1/question_solutions/question_3_trunc_error.py

rukmal/FE-621-Homework

9c7cef7931b58aed54867acd8e8cf1928bc6d2dd

[ "MIT" ]

1

2020-04-23T07:32:44.000Z

from context import fe621 import numpy as np import pandas as pd def truncationErrorAnalysis(): """Function to analyze the truncation error of the Trapezoidal and Simpson's quadature rules. """ # Objective function def f(x: float) -> float: return np.where(x == 0.0, 1.0, np.sin(x) / x) # Setting values for N N = np.power(10, np.arange(3, 8)) # Setting values for a a = np.power(10, np.arange(2, 7)) trapezoidal_vals = np.ndarray((N.size, a.size)) simpsons_vals = np.ndarray((N.size, a.size)) # Building function approximation table, varying N and A for i in range(0, N.size): for j in range(0, a.size): # Trapezoidal rule approximation trapezoidal_vals[i, j] = fe621.numerical_integration \ .trapezoidalRule(f=f, N=N[i], start=-a[j], stop=a[j]) # Simpsons rule trunc approximation simpsons_vals[i, j] = fe621.numerical_integration \ .simpsonsRule(f=f, N=N[i], start=-a[j], stop=a[j]) # Computing the absolute difference from Pi (i.e. trunc error) # and casting to DataFrame trapezoidal_df = pd.DataFrame(np.abs(trapezoidal_vals - np.pi)) simpsons_df = pd.DataFrame(np.abs(simpsons_vals - np.pi)) # Setting row and column names trapezoidal_df.columns = ['N = ' + str(i) for i in N] trapezoidal_df.index = ['a = ' + str(i) for i in a] simpsons_df.columns = ['N = ' + str(i) for i in N] simpsons_df.index = ['a = ' + str(i) for i in a] # Saving to CSV trapezoidal_df.to_csv( 'Homework 1/bin/numerical_integration/trapezoidal_trunc_error.csv', header=True, index=True, float_format='%.8e' ) simpsons_df.to_csv( 'Homework 1/bin/numerical_integration/simpsons_trunc_error.csv', header=True, index=True, float_format='%.8e' ) if __name__ == '__main__': # Part 2 - Truncation Error Analysis truncationErrorAnalysis()

32.933333

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0.630567

285

1,976

4.252632

0.326316

0.016502

0.024752

0.026403

0.381188

0.323432

0.272277

0.234323

0.169967

0.10396

0

0.018817

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1,976

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0.795699

0.220648

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0

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0.070627

0

1

0.0625

false

0

0.09375

0.03125

0.1875

0

null

0

null

0

1

0

879809aa9a3d9ab1577184c4594c4a71184e8e70

8,244

py

Python

lombscargle/core.py

jakevdp/nfftls

01aebd51189a6ed96e44c58cd55b74b8691cbe77

[ "BSD-3-Clause" ]

2

2017-07-29T13:11:34.000Z

2019-11-12T01:41:42.000Z

lombscargle/core.py

jakevdp/nfftls

01aebd51189a6ed96e44c58cd55b74b8691cbe77

[ "BSD-3-Clause" ]

null

lombscargle/core.py

jakevdp/nfftls

01aebd51189a6ed96e44c58cd55b74b8691cbe77

[ "BSD-3-Clause" ]

null

from astropy.stats import LombScargle as astropy_LombScargle from astropy.stats.lombscargle.core import strip_units from astropy.stats.lombscargle.implementations.main import _get_frequency_grid from .nfftls import lombscargle_nfft class LombScargle(astropy_LombScargle): __doc__ = astropy_LombScargle.__doc__ def power(self, frequency, normalization='standard', method='auto', assume_regular_frequency=False, method_kwds=None): """Compute the Lomb-Scargle power at the given frequencies Parameters ---------- frequency : array_like or Quantity frequencies (not angular frequencies) at which to evaluate the periodogram. Note that in order to use method='fast', frequencies must be regularly-spaced. method : string (optional) specify the lomb scargle implementation to use. Options are: - 'auto': choose the best method based on the input - 'nfft': use the O[N log N] nfft library. - 'fast': use the O[N log N] fast method. Note that this requires evenly-spaced frequencies: by default this will be checked unless ``assume_regular_frequency`` is set to True. - 'slow': use the O[N^2] pure-python implementation - 'cython': use the O[N^2] cython implementation. This is slightly faster than method='slow', but much more memory efficient. - 'chi2': use the O[N^2] chi2/linear-fitting implementation - 'fastchi2': use the O[N log N] chi2 implementation. Note that this requires evenly-spaced frequencies: by default this will be checked unless ``assume_regular_frequency`` is set to True. - 'scipy': use ``scipy.signal.lombscargle``, which is an O[N^2] implementation written in C. Note that this does not support heteroskedastic errors. assume_regular_frequency : bool (optional) if True, assume that the input frequency is of the form freq = f0 + df * np.arange(N). Only referenced if method is 'auto' or 'fast'. normalization : string (optional, default='standard') Normalization to use for the periodogram. Options are 'standard', 'model', 'log', or 'psd'. fit_mean : bool (optional, default=True) if True, include a constant offset as part of the model at each frequency. This can lead to more accurate results, especially in the case of incomplete phase coverage. center_data : bool (optional, default=True) if True, pre-center the data by subtracting the weighted mean of the input data. This is especially important if fit_mean = False method_kwds : dict (optional) additional keywords to pass to the lomb-scargle method Returns ------- power : ndarray The Lomb-Scargle power at the specified frequency """ if method == 'nfft': if self.nterms != 1: raise ValueError("nfft method only works for nterms=1") f0, df, Nf = _get_frequency_grid(strip_units(frequency), assume_regular_frequency) if method_kwds and 'use_fft' in method_kwds: use_fft = method_kwds.pop('use_fft') if use_fft: method_kwds['exponential_sum_method'] = 'nfft' else: method_kwds['exponential_sum_method'] = 'slow' power = lombscargle_nfft(*strip_units(self.t, self.y, self.dy), f0, df, Nf, center_data=self.center_data, fit_mean=self.fit_mean, normalization=normalization, **(method_kwds or {})) return power * self._power_unit(normalization) else: return super(LombScargle, self).power(frequency=frequency, normalization=normalization, method=method, assume_regular_frequency=assume_regular_frequency, method_kwds=method_kwds) def autopower(self, method='auto', method_kwds=None, normalization='standard', samples_per_peak=5, nyquist_factor=5, minimum_frequency=None, maximum_frequency=None): """Compute Lomb-Scargle power at automatically-determined frequencies Parameters ---------- method : string (optional) specify the lomb scargle implementation to use. Options are: - 'auto': choose the best method based on the input - 'nfft': use the O[N log N] nfft library. - 'fast': use the O[N log N] fast method. Note that this requires evenly-spaced frequencies: by default this will be checked unless ``assume_regular_frequency`` is set to True. - 'slow': use the O[N^2] pure-python implementation - 'cython': use the O[N^2] cython implementation. This is slightly faster than method='slow', but much more memory efficient. - 'chi2': use the O[N^2] chi2/linear-fitting implementation - 'fastchi2': use the O[N log N] chi2 implementation. Note that this requires evenly-spaced frequencies: by default this will be checked unless ``assume_regular_frequency`` is set to True. - 'scipy': use ``scipy.signal.lombscargle``, which is an O[N^2] implementation written in C. Note that this does not support heteroskedastic errors. method_kwds : dict (optional) additional keywords to pass to the lomb-scargle method normalization : string (optional, default='standard') Normalization to use for the periodogram. Options are 'standard', 'model', or 'psd'. samples_per_peak : float (optional, default=5) The approximate number of desired samples across the typical peak nyquist_factor : float (optional, default=5) The multiple of the average nyquist frequency used to choose the maximum frequency if maximum_frequency is not provided. minimum_frequency : float (optional) If specified, then use this minimum frequency rather than one chosen based on the size of the baseline. maximum_frequency : float (optional) If specified, then use this maximum frequency rather than one chosen based on the average nyquist frequency. Returns ------- frequency, power : ndarrays The frequency and Lomb-Scargle power """ if method == 'nfft': frequency = self.autofrequency(samples_per_peak=samples_per_peak, nyquist_factor=nyquist_factor, minimum_frequency=minimum_frequency, maximum_frequency=maximum_frequency) power = self.power(frequency, normalization=normalization, method=method, method_kwds=method_kwds, assume_regular_frequency=True) return frequency, power else: return super(LombScargle, self).autopower(method=method, method_kwds=method_kwds, normalization=normalization, samples_per_peak=samples_per_peak, nyquist_factor=nyquist_factor, minimum_frequency=minimum_frequency, maximum_frequency=maximum_frequency)

54.596026

100

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881

8,244

5.240636

0.22815

0.034655

0.018194

0.020793

0.555339

0.501191

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0.42885

0

0.004749

0.361475

8,244

150

101

54.96

0.87234

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0

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0

0.222222

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null

0

null

0

1

0

87993e598fc65ff1079e82a8b6905aa0e27e9001

1,188

py

Python

models/accent_model.py

guyeshet/Keras-Project-Template

4b324aea4a923ca0ceb1610487bf7139706fae33

[ "Apache-2.0" ]

null

models/accent_model.py

guyeshet/Keras-Project-Template

4b324aea4a923ca0ceb1610487bf7139706fae33

[ "Apache-2.0" ]

null

models/accent_model.py

guyeshet/Keras-Project-Template

4b324aea4a923ca0ceb1610487bf7139706fae33

[ "Apache-2.0" ]

null

import librosa from base.base_model import BaseModel from keras.models import Sequential from keras.layers import Dense, Conv2D, MaxPooling2D, Dropout, Flatten class AccentModel(BaseModel): def __init__(self, config): super(AccentModel, self).__init__(config) self.build_model() def build_model(self): self.model = Sequential() self.model.add(Conv2D(32, kernel_size=(3, 3), activation='relu', data_format="channels_last", input_shape=self.config.model.input_shape)) self.model.add(MaxPooling2D(pool_size=(2, 2))) self.model.add(Conv2D(64, kernel_size=(3, 3), activation='relu')) self.model.add(MaxPooling2D(pool_size=(2, 2))) self.model.add(Dropout(rate=0.25)) self.model.add(Flatten()) self.model.add(Dense(128, activation='relu')) self.model.add(Dropout(rate=0.5)) self.model.add(Dense(self.config.model.num_classes, activation='softmax')) self.model.compile(loss=self.config.model.loss, optimizer=self.config.model.optimizer, metrics=['accuracy'])

33

82

0.625421

144

1,188

5.020833

0.361111

0.136929

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0.049793

0.268326

0.248963

0.127248

0

0.029018

0.245791

1,188

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33.942857

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0

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0

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0

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0.083333

false

0

0.166667

0

0.291667

0

null

0

null

0

1

0

879ac40f2728ea332bcf4325e96c15745d65d3e6

5,480

py

Python

recipesAPI/users/views.py

rainsha/polsl_mgr_obierki

cc5ddb3df7b6c75f385f64029a10bd4919827545

[ "MIT" ]

null

recipesAPI/users/views.py

rainsha/polsl_mgr_obierki

cc5ddb3df7b6c75f385f64029a10bd4919827545

[ "MIT" ]

null

recipesAPI/users/views.py

rainsha/polsl_mgr_obierki

cc5ddb3df7b6c75f385f64029a10bd4919827545

[ "MIT" ]

null

from django.core.serializers.json import DjangoJSONEncoder from django.http import JsonResponse, HttpResponse, Http404 from drf_yasg.openapi import Schema, TYPE_STRING, TYPE_OBJECT from drf_yasg.utils import swagger_auto_schema from rest_framework import status, serializers, permissions from rest_framework.generics import GenericAPIView import json from rest_framework.views import APIView from rest_framework_simplejwt.tokens import RefreshToken from rest_framework_simplejwt.views import TokenRefreshView from users.models import User from users.serializers import * from django.contrib import auth class RegisterView(GenericAPIView): serializer_class = UserSerializer permission_classes = [permissions.AllowAny] @swagger_auto_schema(tags=["user"]) def post(self, request): serializer = UserSerializer(data=request.data) if serializer.is_valid(): serializer.save() data = {'isCreated': True} return JsonResponse(data, status=status.HTTP_201_CREATED) data = {'isCreated': False, 'errorMessage': serializer.errors} return JsonResponse(data, status=status.HTTP_400_BAD_REQUEST) class LoginView(GenericAPIView): serializer_class = LoginSerializer permission_classes = [permissions.AllowAny] @swagger_auto_schema(tags=["user"], request_body=Schema( type=TYPE_OBJECT, properties={ 'username': Schema(type=TYPE_STRING), 'password': Schema(type=TYPE_STRING), } ) ) def post(self, request): data = request.data username = data.get('username', '') password = data.get('password', '') user = auth.authenticate(username=username, password=password) if user: token = RefreshToken.for_user(user) data = {'id': user.id, 'name': user.username, 'accessToken': str(token.access_token), 'refreshToken': str(token)} return JsonResponse(data, status=status.HTTP_200_OK) return JsonResponse({'errorMessage': 'Invalid credentials'}, status=status.HTTP_401_UNAUTHORIZED) class UserDetail(APIView): """ Retrieve, update or delete a user instance. """ def get_object(self, pk): try: return User.objects.get(pk=pk) except User.DoesNotExist: raise Http404 def get(self, request, pk, format=None): try: user = self.get_object(pk) except Http404: return JsonResponse({'isDeleted': False, 'errorMessage': "User does not exist"}, safe=False, status=status.HTTP_404_NOT_FOUND) return JsonResponse({'id': user.id, 'username': user.username}, safe=False, status=status.HTTP_200_OK) def put(self, request, pk, format=None): try: user = self.get_object(pk) except Http404: return JsonResponse({'isUpdated': False, 'errorMessage': "User does not exists"}, safe=False, status=status.HTTP_404_NOT_FOUND) user_serializer = UserSerializer(user, data=request.data) try: if user_serializer.is_valid(raise_exception=True): user_serializer.save() return JsonResponse({'isUpdated': True, 'errorMessage': ""}, safe=False, status=status.HTTP_200_OK) except serializers.ValidationError as valEr: return JsonResponse({'isUpdated': False, 'errorMessage': valEr.detail}, safe=False, status=status.HTTP_400_BAD_REQUEST) def delete(self, request, pk, format=None): try: user = self.get_object(pk) except Http404: return JsonResponse({'isDeleted': False, 'errorMessage': "User does not exist"}, safe=False, status=status.HTTP_404_NOT_FOUND) user.delete() return JsonResponse({'isDeleted': True, 'errorMessage': ""}, safe=False, status=status.HTTP_204_NO_CONTENT) class UserList(APIView): """ Create or get user instance. """ def user_exists_by_name(self, name): return User.objects.filter(username=name).exists() def get(self, request, format=None): users = User.objects.values('id', 'username') data = json.dumps(list(users), cls=DjangoJSONEncoder) return HttpResponse(data, content_type="application/json") def post(self, request, pk, format=None): user_serializer = UserSerializer(data=request.data) try: if user_serializer.is_valid(raise_exception=True): if not self.user_exists_by_name(user_serializer.validated_data['username']): user_serializer.save() return JsonResponse({'isCreated': True, 'errorMessage': ""}, safe=False, status=status.HTTP_201_CREATED) return JsonResponse({'isCreated': False, 'errorMessage': user_serializer.errors}, status=status.HTTP_400_BAD_REQUEST) except serializers.ValidationError as valEr: return JsonResponse({'isCreated': False, 'errorMessage': valEr.detail}, safe=False, status=status.HTTP_400_BAD_REQUEST)

41.515152

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0.622993

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0

null

0

null

0

1

0

879bb779932f5bf1086d8b00e1c10c5d98c48288

5,267

py

Python

setup.py

shengdexiang/Office16CustomInstaller

9032620470aa44beee6be1e55e2f47abecb9b7e1

[ "Apache-2.0" ]

null

setup.py

shengdexiang/Office16CustomInstaller

9032620470aa44beee6be1e55e2f47abecb9b7e1

[ "Apache-2.0" ]

null

setup.py

shengdexiang/Office16CustomInstaller

9032620470aa44beee6be1e55e2f47abecb9b7e1

[ "Apache-2.0" ]

null

#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ This script enable custom installation of Microsoft Office suite. You can install/uninstall specific product. """ import argparse import os import sys import xml.etree.ElementTree as ET ALL_PRODUCTS = ['Word', 'Excel', 'PowerPoint', 'Access', 'Groove', 'InfoPath', 'Lync', 'OneNote', 'Project', 'Outlook', 'Publisher', 'Visio', 'SharePointDesigner', 'OneDrive'] class Setup(object): """Microsoft Office 2016 custom installer wrapper.""" def __init__(self, args): self.args = args self.config_file = 'configuration.xml' self.lang = self._get_product_lang() self.all_products = ALL_PRODUCTS self.product_list_to_install = self._get_product_list() self.product_edition = self._get_product_edition() self._init_config_file() self._gen_config_file() def _get_product_list(self): """Get products to be installed/uninstalled.. Get products to be installed/uninstalled. Args: None. Returns: None. """ product_list = [] if ',' in self.args.product: product_list.extend(self.args.product.split(',')) else: product_list.append(self.args.product) return product_list def _get_product_edition(self): """Get product edition to be used. Get product edition to be used. Args: None. Returns: None. """ return self.args.edition def _get_product_lang(self): """Get product language to be used. Get product language to be used. Args: None. Returns: None. """ return self.args.lang def _init_config_file(self): """Initialize configuration file. Initialize configuration file template. Args: None. Returns: None. """ print('Initializing Configuration File'.center(60, '=')) init_xml_str = ('<Configuration>\n' ' <Add SourcePath="Office" Branch="Current" OfficeClientEdition="64">\n' ' <Product ID="ProPlusRetail">\n' ' <Language ID="zh-cn" />\n' ' </Product>\n' ' </Add>\n' '</Configuration>\n') if os.path.exists(self.config_file): os.unlink(self.config_file) open(self.config_file, 'w').write(init_xml_str + '\n') def _gen_config_file(self): """Generate configuration file. Generate configuration file, which will be used to custom installation. Args: None. Returns: None. """ print('Generating Configuration File'.center(60, '=')) tree = ET.parse(self.config_file) root = tree.getroot() # update product edition root[0].set('OfficeClientEdition', self.product_edition) # update product language for lang in root.iter('Language'): lang.set('ID', self.lang) # update product that will not be installed for product in root.iter('Product'): for item in self.all_products: if item not in self.product_list_to_install: app = ET.SubElement(product, 'ExcludeApp') app.set('ID', item) ET.dump(root) tree.write(self.config_file) def run(self): """Class entry point. Args: None. Returns: None. """ if self.args.action == 'download': os.system('.\setup.exe /download {0}'.format(self.config_file)) elif self.args.action == 'install': os.system('.\setup.exe /configure {0}'.format(self.config_file)) else: pass os.unlink(self.config_file) def get_argparser(): """Generate a command line argument parser.""" parser = argparse.ArgumentParser( prog=sys.argv[0], description='Microsoft Office 2016 downloader/installer', epilog=('e.g.: python setup.py --action install --product word ' '--edition 64 --lang zh-cn')) parser.add_argument('-a', '--action', action='store', default='install', help='install | download') parser.add_argument('-p', '--product', action='store', required=True, choices=ALL_PRODUCTS, help='product to install') parser.add_argument('-e', '--edition', action='store', default='64', help='product edition, e.g. 64/32') parser.add_argument('-l', '--lang', action='store', default='zh-cn', help='install language, e.g. en-us/zh-cn') return parser def main(): """Program entry point.""" parser = get_argparser() args = parser.parse_args() if (args.action is None or args.product is None or args.lang is None or args.edition is None): parser.print_usage() sys.exit(1) setup = Setup(args) setup.run() if __name__ == '__main__': main()

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4.949565

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0.045678

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0.040056

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0.102249

0.028812

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0.008126

0.322385

5,267

184

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0

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0.102273

false

0.011364

0.045455

0

0.204545

0.034091

0

null

0

null

0

1

0

879d115e1b51db4348e7e20329e0ed60fbefd5d2

954

py

Python

arike/visits/urls.py

iamsdas/arike

ab76f48f49cd794dd4b77172b347e260a03413b2

[ "MIT" ]

null

arike/visits/urls.py

iamsdas/arike

ab76f48f49cd794dd4b77172b347e260a03413b2

[ "MIT" ]

null

arike/visits/urls.py

iamsdas/arike

ab76f48f49cd794dd4b77172b347e260a03413b2

[ "MIT" ]

null

from django.urls import path from arike.visits.views import ( ScheduleCreateView, ScheduleDeleteView, ScheduleDetailView, ScheduleListVeiw, ScheduleUpdateView, TreatmentNoteCreateView, TreatmentsListVeiw, VisitDetailsCreateView, ) app_name = "visits" urlpatterns = [ path("schedule/", view=ScheduleListVeiw.as_view(), name="list"), path("create/", view=ScheduleCreateView.as_view(), name="create"), path("<pk>/visit/", view=VisitDetailsCreateView.as_view(), name="visit"), path("<pk>/", view=ScheduleDetailView.as_view(), name="view"), path("<pk>/update/", view=ScheduleUpdateView.as_view(), name="update"), path("<pk>/delete/", view=ScheduleDeleteView.as_view(), name="delete"), path("<pk>/treatments/", view=TreatmentsListVeiw.as_view(), name="treatments"), path( "<pk>/treatments/<id>/addnote", view=TreatmentNoteCreateView.as_view(), name="add_note", ), ]

32.896552

83

0.681342

94

954

6.808511

0.351064

0.075

0.125

0

0.150943

954

28

84

34.071429

0.790123

0

0.162474

0.02935

0

1

0

false

0

0.076923

0

0.076923

0

null

0

null

0

1

0

879f5cb728abbf0e5820bf60a16e817ad084aa07

1,790

py

Python

sketchify/xdog_blend.py

DBSAMOR/sketch-i2v

222f06a1983c5b5bb87ce8c74f1d987c2b3d99ed

[ "MIT" ]

232

2019-08-19T01:11:24.000Z

2022-03-21T13:53:09.000Z

sketchify/xdog_blend.py

DBSAMOR/sketch-i2v

222f06a1983c5b5bb87ce8c74f1d987c2b3d99ed

[ "MIT" ]

4

2019-09-02T03:20:00.000Z

2020-02-04T05:27:30.000Z

sketchify/xdog_blend.py

DBSAMOR/sketch-i2v

222f06a1983c5b5bb87ce8c74f1d987c2b3d99ed

[ "MIT" ]

34

2019-08-19T10:14:26.000Z

2022-03-23T01:05:43.000Z

import cv2 import numpy as np from scipy import ndimage def dog(img, size=(0,0), k=1.6, sigma=0.5, gamma=1): img1 = cv2.GaussianBlur(img, size, sigma) img2 = cv2.GaussianBlur(img, size, sigma * k) return (img1 - gamma * img2) def xdog(img, sigma=0.5, k=1.6, gamma=1, epsilon=1, phi=1): aux = dog(img, sigma=sigma, k=k, gamma=gamma) / 255 for i in range(0, aux.shape[0]): for j in range(0, aux.shape[1]): if(aux[i, j] < epsilon): aux[i, j] = 1*255 else: aux[i, j] = 255*(1 + np.tanh(phi * (aux[i, j]))) return aux def get_xdog_image(img, sigma=0.4, k=2.5, gamma=0.95, epsilon=-0.5, phi=10**9): xdog_image = xdog(img, sigma=sigma, k=k, gamma=gamma, epsilon=epsilon, phi=phi).astype(np.uint8) return xdog_image def add_intensity(img, intensity): if intensity == 1: return img inten_const = 255.0 ** (1 - intensity) return (inten_const * (img ** intensity)).astype(np.uint8) def blend_xdog_and_sketch(illust, sketch, intensity=1.7, degamma=(1/1.5), blend=0, **kwargs): gray_image = cv2.cvtColor(illust, cv2.COLOR_BGR2GRAY) gamma_sketch = add_intensity(sketch, intensity) if blend > 0: xdog_image = get_xdog_image(gray_image, **kwargs) xdog_blurred = cv2.GaussianBlur(xdog_image, (5, 5), 1) xdog_residual_blur = cv2.addWeighted(xdog_blurred, 0.75, xdog_image, 0.25, 0) if gamma_sketch.shape != xdog_residual_blur.shape: gamma_sketch = cv2.resize(gamma_sketch, xdog_residual_blur.shape, interpolation=cv2.INTER_AREA) blended_image = cv2.addWeighted(xdog_residual_blur, blend, gamma_sketch, (1-blend), 0) else: blended_image = gamma_sketch return add_intensity(blended_image, degamma)

38.913043

107

0.643575

281

1,790

3.957295

0.256228

0.056655

0.017986

0.039568

0.122302

0.044964

0

0.059243

0.217318

1,790

46

108

38.913043

0.734475

0

0.054054

0

1

0.135135

false

0

0.081081

0

0.378378

0

null

0

null

0

1

0

87a08ee0b9a91c3600f9993ce0331c05385252cf

3,033

py

Python

tests/setup_parser.py

jcao1022/tiflash

841d4d2c6eeba35ef62c7a8feefc476182175e3d

[ "MIT" ]

1

2019-04-11T05:52:56.000Z

tests/setup_parser.py

jcao1022/tiflash

841d4d2c6eeba35ef62c7a8feefc476182175e3d

[ "MIT" ]

null

tests/setup_parser.py

jcao1022/tiflash

841d4d2c6eeba35ef62c7a8feefc476182175e3d

[ "MIT" ]

null

import os import platform try: from ConfigParser import ConfigParser except ImportError: from configparser import ConfigParser class TestSetupError(Exception): """Generic Error with parsing Test Setup configuration""" pass class TestSetup(object): """Class used for accessing various settings in test setup configuartion file: setup.cfg """ def __init__(self): self.cfg = ConfigParser(allow_no_value=True) self.cfg.optionxform = str self.cfg.read("./setup.cfg") def get_ccs_prefix(self): """Returns the set ccs_prefix Returns: str: ccs_prefix variable set in setup.cfg """ return self.cfg.get('environment', 'ccs_prefix') def get_ccs_versions(self): """Returns a tuple of CCS versions installed Returns: tuple: a tuple of ints representing CCS versions installed in test setup """ versions = map(str.strip, self.cfg.get('environment', 'ccs_versions').split(',')) return tuple(versions) def get_ccs_installs(self): """Returns a tuple of all CCS install paths Returns: tuple: a tuple of strs being the full paths to each CCS installation """ system = platform.system() versions = self.get_ccs_versions() ccs_paths = map(str.strip, self.cfg.get('environment', 'ccs_installs').split(',')) ccs_paths = tuple(ccs_paths) for path in ccs_paths: if not os.path.exists(path): raise TestSetupError("CCS Install: %s could not be found. " "Remove this ccs version from setup.cfg" % path) return tuple(ccs_paths) def get_target_config_directory(self): """Returns the target configuation directory Returns: str: Path to target configuration directory """ ccxml_dir = self.cfg.get("environment", "ccxml_dir") if not os.path.exists(ccxml_dir): raise TestSetupError("Target Config Directory: %s could not" " be found." % ccxml_dir) return ccxml_dir def get_devices(self): """Returns a dict of devices with specified configurations (devices.cfg) Returns: dict: dict of device dicts in format: { devicename: { serno: SERNO, connection: CONN, devicetype: DEVTYPE } } """ devices = dict() device_list = [ dev for dev in self.cfg.options('devices') if self.cfg.getboolean('devices', dev) ] for devname in device_list: dev = dict() options = self.cfg.options(devname) for o in options: dev[o] = self.cfg.get(devname, o) devices[devname] = dev return devices

28.885714

90

0.563798

333

3,033

5.03003

0.321321

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0.029851

0.050149

0.14209

0.041791

0

0.351137

3,033

104

91

29.163462

0.851118

0.285856

0

0.122141

0

1

0.130435

false

0.021739

0.108696

0

0.391304

0

null

0

null

0

1

0

87a0b5c529797765dcec85fd5ef6d46f2a931cfb

3,238

py

Python

vrs/resolver.py

open-voice-network/pyvrs

73898c53f67d1ad5798e4c39c366a2c168108bfc

[ "Apache-2.0" ]

null

vrs/resolver.py

open-voice-network/pyvrs

73898c53f67d1ad5798e4c39c366a2c168108bfc

[ "Apache-2.0" ]

2

2022-01-18T16:18:12.000Z

2022-03-28T16:08:43.000Z

vrs/resolver.py

open-voice-network/pyvrs

73898c53f67d1ad5798e4c39c366a2c168108bfc

[ "Apache-2.0" ]

null

import base64 import dns.resolver import json import logging import requests import shlex from vrs import is_base64, is_json from configparser import ConfigParser logger = logging.getLogger('pyvrs') class VRSDecodeError(Exception): """Catchall VRS error for decoding issues.""" class Resolver: """Base resolver class.""" class RESTResolver(Resolver): def __init__(self, conf): self.conf = conf self.url = self.conf['url'] self.email = self.conf['email'] self.password = self.conf['password'] self.session = requests.Session() def login(self): login_url = f"{self.url}/api/login" login_data = {'email': self.email, 'password': self.password} logger.debug(f'{login_url} {login_data}') response = self.session.post(login_url, json=login_data) response.raise_for_status() def resolve(self, name): """Resolve keywords against known ReST APIs.""" try: self.login() records_url = f"{self.url}/api/records/{name}" logger.debug(f'querying: {records_url}') response = self.session.get(records_url) logger.debug(f'response: {response}') response.raise_for_status() yield response.text except Exception as e: logger.warn(f"{e}") yield class DNSResolver(Resolver): def __init__(self, conf): self.conf = conf def resolve(self, name): """Resolve any TXT records in <subdomain>.<domain>""" try: concat = name + "." + self.conf["hostname"] answers = dns.resolver.resolve(concat, 'TXT') logger.debug(f'querying: {answers.qname}') for a in answers: yield self.decode(a) except Exception as e: logger.warn(e) yield def decode(self, rdata): logger.debug(f"rdata: '{rdata}'") try: txt = (rdata.to_text().encode('raw_unicode_escape') .decode('unicode_escape').strip("'\"")) logger.debug(f"txt: '{txt}'") if is_base64(txt): return str(base64.b64decode(txt), 'utf8').strip() elif is_json(txt): return json.loads(txt) elif all([r in txt for r in ('dest', 'name', 'country')]): # this is in plaintext, not encoded d = {} for i in shlex.split(txt): d.update([i.split("=")]) return d else: return rdata.strings except Exception as ex: raise VRSDecodeError(ex) def GetResolver(conf): if 'password' in conf: return RESTResolver(conf) elif 'hostname' in conf: return DNSResolver(conf) else: raise Exception(f"Invalid config block: {conf}") def resolve(name, conf): """Resolve the name via each resolver config block.""" cp = ConfigParser() cp.read(conf) for section in cp.sections(): resolver = GetResolver(cp[section]) logger.debug(f"resolving with section [{section}] ==> {resolver}") for record in resolver.resolve(name): yield record

29.171171

74

0.57134

372

3,238

4.895161

0.306452

0.035146

0.046129

0.020868

0.112026

0.069193

0.03844

0

0.004895

0.306053

3,238

110

75

29.436364

0.805518

0.072267

0

0.204819

0

0.122565

0.009738

0

1

0.096386

false

0.036145

0.096386

0

0.313253

0

null

0

null

0

1

0

87a167db0b2bc93064d8e1008b52e1b7af27bbdd

1,166

py

Python

test/writing/test_template.py

patricksanders/policy_sentry

3559ce8d3a19728b4f64dfff4cbdf075e7629b39

[ "MIT" ]

null

test/writing/test_template.py

patricksanders/policy_sentry

3559ce8d3a19728b4f64dfff4cbdf075e7629b39

[ "MIT" ]

20

2020-03-20T06:13:09.000Z

2022-02-10T18:15:35.000Z

test/writing/test_template.py

ssmbct-netops/policy_sentry

c9f4752c633fe229220b7f476aa766ea65330489

[ "MIT" ]

null

import unittest from policy_sentry.writing.template import create_actions_template, create_crud_template class TemplateTestCase(unittest.TestCase): def test_actions_template(self): desired_msg = """# Generate my policy when I know the Actions mode: actions name: myrole description: '' # For human auditability role_arn: '' # For human auditability actions: - ''""" actions_template = create_actions_template("myrole") self.assertEqual(desired_msg, actions_template) def test_crud_template(self): desired_msg = """# Generate my policy when I know the access levels and ARNs mode: crud name: myrole description: '' # For human auditability role_arn: '' # For human auditability # Insert ARNs under each access level below # If you do not need to use certain access levels, delete them. read: - '' write: - '' list: - '' tagging: - '' permissions-management: - '' # If the policy needs to use IAM actions that cannot be restricted to ARNs, # like ssm:DescribeParameters, specify those actions here. wildcard: - ''""" crud_template = create_crud_template("myrole") self.assertEqual(desired_msg, crud_template)

28.439024

88

0.734991

149

1,166

5.597315

0.483221

0.089928

0.095923

0.06235

0.376499

0.282974

0

0.174099

1,166

40

89

29.15

0.866044

0

0.351351

0

0.577187

0.039451

0

0.054054

1

0.054054

false

0

0.054054

0

0.135135

0

null

0

null

0

1

0

87a60c95cfcb01535bd33a02ec71b3ce88f7fdc1

2,566

py

Python

examples/graph/test_dice.py

HenryKenlay/DeepRobust

ea8871d970257a9c11715cd059a5331177a00395

[ "MIT" ]

1

2020-06-12T07:45:06.000Z

examples/graph/test_dice.py

lorenzobasile/DeepRobust

3f56dcc45f1fed788423d32cc179c26513416e2e

[ "MIT" ]

null

examples/graph/test_dice.py

lorenzobasile/DeepRobust

3f56dcc45f1fed788423d32cc179c26513416e2e

[ "MIT" ]

null

import torch import numpy as np import torch.nn.functional as F import torch.optim as optim from deeprobust.graph.defense import GCN from deeprobust.graph.global_attack import DICE from deeprobust.graph.utils import * from deeprobust.graph.data import Dataset import argparse parser = argparse.ArgumentParser() parser.add_argument('--seed', type=int, default=15, help='Random seed.') parser.add_argument('--dataset', type=str, default='citeseer', choices=['cora', 'cora_ml', 'citeseer', 'polblogs', 'pubmed'], help='dataset') parser.add_argument('--ptb_rate', type=float, default=0.05, help='pertubation rate') args = parser.parse_args() args.cuda = torch.cuda.is_available() print('cuda: %s' % args.cuda) device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") np.random.seed(args.seed) torch.manual_seed(args.seed) if args.cuda: torch.cuda.manual_seed(args.seed) data = Dataset(root='/tmp/', name=args.dataset) adj, features, labels = data.adj, data.features, data.labels idx_train, idx_val, idx_test = data.idx_train, data.idx_val, data.idx_test idx_unlabeled = np.union1d(idx_val, idx_test) # Setup Attack Model model = DICE() n_perturbations = int(args.ptb_rate * (adj.sum()//2)) modified_adj = model.attack(adj, labels, n_perturbations) adj, features, labels = preprocess(adj, features, labels, preprocess_adj=False, sparse=True, device=device) modified_adj = normalize_adj(modified_adj) modified_adj = sparse_mx_to_torch_sparse_tensor(modified_adj) modified_adj = modified_adj.to(device) def test(adj): ''' test on GCN ''' # adj = normalize_adj_tensor(adj) gcn = GCN(nfeat=features.shape[1], nhid=16, nclass=labels.max().item() + 1, dropout=0.5, device=device) gcn = gcn.to(device) optimizer = optim.Adam(gcn.parameters(), lr=0.01, weight_decay=5e-4) gcn.fit(features, adj, labels, idx_train) # train without model picking # gcn.fit(features, adj, labels, idx_train, idx_val) # train with validation model picking output = gcn.output loss_test = F.nll_loss(output[idx_test], labels[idx_test]) acc_test = accuracy(output[idx_test], labels[idx_test]) print("Test set results:", "loss= {:.4f}".format(loss_test.item()), "accuracy= {:.4f}".format(acc_test.item())) return acc_test.item() def main(): print('=== testing GCN on original(clean) graph ===') test(adj) print('=== testing GCN on perturbed graph ===') test(modified_adj) if __name__ == '__main__': main()

32.075

141

0.697973

367

2,566

4.711172

0.340599

0.050896

0.043956

0.038172

0.142857

0.065934

0.035859

0

0.009731

0.159002

2,566

79

142

32.481013

0.791474

0.070148

0

0.108677

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0.036364

false

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0.163636

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0.218182

0.072727

0

null

0

null

0

1

0

87a93beab125fd0c5d8d7106fb3c4f852c610456

3,742

py

Python

mzmlripper/logger.py

croningp/mzmlripper

f8c75a3380c9502815a8df4bdf45d372c31275ed

[ "MIT" ]

1

2021-06-15T03:04:04.000Z

mzmlripper/logger.py

croningp/mzmlripper

f8c75a3380c9502815a8df4bdf45d372c31275ed

[ "MIT" ]

null

mzmlripper/logger.py

croningp/mzmlripper

f8c75a3380c9502815a8df4bdf45d372c31275ed

[ "MIT" ]

2

2021-05-08T06:31:47.000Z

2021-06-15T03:03:20.000Z

""" .. module:: prototools.logger :platforms: Unix :synopsis: Custom logger with ANSI coloring .. moduleauthor:: Graham Keenan 2020 """ # System imports import time import logging from typing import Optional ANSI_COLORS = { 'black': '\u001b[30m', 'red': '\u001b[31m', 'green': '\u001b[32m', 'yellow': '\u001b[33m', 'blue': '\u001b[34m', 'magenta': '\u001b[35m', 'cyan': '\u001b[36m', 'white': '\u001b[37m', 'bold': '\u001b[1m', 'reset': '\u001b[0m' } def colour_item( msg: str, color: Optional[str] = '', bold: Optional[bool] = False ) -> str: """Colours a message with an ANSI color and escapes it at the end. Options for bold text. Args: msg (str): Message to colour color (str): Colour of the text bold (Optional[bool], optional): Bold the message. Defaults to False. Returns: str: ANSI formatted message """ color = ANSI_COLORS[color] if color in ANSI_COLORS else '' return ( f'{color}{ANSI_COLORS["bold"]}{msg}{ANSI_COLORS["reset"]}' if bold else f'{color}{msg}{ANSI_COLORS["reset"]}' ) def make_logger( name: str, filename: Optional[str] = '', debug: Optional[bool] = False ) -> logging.Logger: """Creates a logger using the custom ProtoFormatter with options for file output. Args: name (str): Name of the logger filename (Optional[str], optional): Output log file. Defaults to ''. debug (Optional[bool], optional): Debug mode. Defaults to False. Returns: logging.Logger: Logger """ # Get logger and set level logger = logging.getLogger(name) level = logging.DEBUG if debug else logging.INFO logger.setLevel(level) # Custom ANSI colour formatter formatter = ProtoFormatter() # Using file logging, add FileHandler if filename: fh = logging.FileHandler(filename=filename) fh.setLevel(level) fh.setFormatter(formatter) logger.addHandler(fh) # Setup stream handler sh = logging.StreamHandler() sh.setLevel(level) sh.setFormatter(formatter) logger.addHandler(sh) logger.propagate = False return logger class ProtoFormatter(logging.Formatter): """Custom Formatter to support ANSI colouring Inherits: logging.Formatter: Base Formatter """ def __init__(self): super().__init__() def format(self, record: logging.LogRecord) -> str: """Formats the LogRecord with custom formatting Args: record (logging.LogRecord): Record to format Returns: str: Formatted Text """ # Get level and level number level, levelno, msg = record.levelname, record.levelno, record.msg # Colour level name depending on level severity if levelno == logging.DEBUG: level = colour_item(level, color='red') elif levelno == logging.INFO: level = colour_item(level, color='green') elif levelno == logging.WARN: level = colour_item(level, color='yellow', bold=True) msg = colour_item(msg, color='yellow') elif levelno == logging.ERROR: level = colour_item(level, color='red', bold=True) msg = colour_item(msg, color='red', bold=True) elif levelno == logging.CRITICAL: level = colour_item(level, color='red', bold=True) msg = colour_item(msg, color='red') # Log the current time timestamp = time.strftime('%d-%m-%Y|%H:%M:%S') # Colour the logger name name = colour_item(record.name, color='cyan') # Formatted message return f'[{timestamp}] - {name}::{level} -- {msg}'

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0.611972

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3,742

5.171233

0.312785

0.04415

0.033113

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0.100221

0.078146

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null

0

null

0

1

0

87a9a06fcaf0be41d44e4b1eb59937be34e9e629

3,236

py

Python

ros/fetch_vr/scripts/camera_transform_publisher.py

scottwillmoore/fetch_vr

22c28e6c1d95655806ea2667a4397556bbddd580

[ "MIT" ]

1

2022-03-06T15:24:38.000Z

ros/fetch_vr/scripts/camera_transform_publisher.py

scottwillmoore/fetch_vr

22c28e6c1d95655806ea2667a4397556bbddd580

[ "MIT" ]

null

ros/fetch_vr/scripts/camera_transform_publisher.py

scottwillmoore/fetch_vr

22c28e6c1d95655806ea2667a4397556bbddd580

[ "MIT" ]

null

#!/usr/bin/env python import aruco_msgs.msg import geometry_msgs.msg import rospy import tf2_ros from tf import transformations as t class ExternalCamera(): def __init__(self): rospy.init_node("camera_transform_publisher") one_time = rospy.get_param("~one_time", False) rate = rospy.get_param("~rate", 10) time_out = rospy.get_param("~time_out", 10.0) assert isinstance(one_time, bool) assert isinstance(rate, int) assert isinstance(time_out, float) rospy.loginfo("one_time: %s", one_time) rospy.loginfo("Rate: %f" % rate) rospy.loginfo("Time out: %d" % time_out) first_time = True rate = rospy.Rate(rate) self.transform_broadcaster = tf2_ros.StaticTransformBroadcaster() self.marker_subscriber = rospy.Subscriber("external_camera_marker_publisher/markers", aruco_msgs.msg.MarkerArray, self.callback) def callback(self, marker_array): # while not rospy.is_shutdown(): # # if not one_time or first_time: # first_time = False # try: # marker_array = rospy.wait_for_message("external_camera_marker_publisher/markers", aruco_msgs.msg.MarkerArray, time_out) # except rospy.ROSException: # rospy.logerr("Timed out!") # raise for marker in marker_array.markers: if marker.id == 200: transform = geometry_msgs.msg.TransformStamped() transform.header.stamp = rospy.Time.now() transform.header.frame_id = "marker_200" transform.child_frame_id = "camera_link" trans = (marker.pose.pose.position.x, marker.pose.pose.position.y, marker.pose.pose.position.z) rot = (marker.pose.pose.orientation.x, marker.pose.pose.orientation.y, marker.pose.pose.orientation.z, marker.pose.pose.orientation.w) transform_mat = t.concatenate_matrices(t.translation_matrix(trans), t.quaternion_matrix(rot)) inv_transform_mat = t.inverse_matrix(transform_mat) inv_trans = t.translation_from_matrix(inv_transform_mat) inv_rot = t.quaternion_from_matrix(inv_transform_mat) transform.transform.translation.x = inv_trans[0] transform.transform.translation.y = inv_trans[1] transform.transform.translation.z = inv_trans[2] transform.transform.rotation.x = inv_rot[0] transform.transform.rotation.y = inv_rot[1] transform.transform.rotation.z = inv_rot[2] transform.transform.rotation.w = inv_rot[3] try: self.transform_broadcaster.sendTransform(transform) print("Published transform from marker to external camera\n") except: print("Marker not yet detected by camera\n") # self.rate.sleep() if __name__ == "__main__": try: ExternalCamera() rospy.spin() except rospy.ROSInterruptException: pass

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0.301607

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false

0.019231

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0.038462

0

null

0

null

0

1

0

87aa4958b8bdc3ba6a551199bf30e998942db846

2,418

py

Python

src/clusterfuzz/_internal/tests/core/metrics/fuzzer_logs_test.py

mspectorgoogle/clusterfuzz

44df69cbcb94efc212f27758d45d6ff0f36061e5

[ "Apache-2.0" ]

5,023

2019-02-07T16:57:56.000Z

2022-03-31T01:08:05.000Z

src/clusterfuzz/_internal/tests/core/metrics/fuzzer_logs_test.py

mspectorgoogle/clusterfuzz

44df69cbcb94efc212f27758d45d6ff0f36061e5

[ "Apache-2.0" ]

2,303

2019-02-07T17:36:36.000Z

2022-03-31T15:44:38.000Z

src/clusterfuzz/_internal/tests/core/metrics/fuzzer_logs_test.py

mspectorgoogle/clusterfuzz

44df69cbcb94efc212f27758d45d6ff0f36061e5

[ "Apache-2.0" ]

564

2019-02-07T17:34:24.000Z

2022-03-26T09:25:44.000Z

# Copyright 2019 Google LLC # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """fuzzer_logs test.""" import datetime import unittest import mock from clusterfuzz._internal.metrics import fuzzer_logs from clusterfuzz._internal.system import environment from clusterfuzz._internal.tests.test_libs import helpers as test_helpers class FuzzerLogsTest(unittest.TestCase): """Tests for logs uploading.""" def setUp(self): test_helpers.patch_environ(self) environment.set_value('FUZZER_NAME', 'fuzzer_1') environment.set_value('JOB_NAME', 'fake_job') # To be used for generation of date and time when uploading a log. self.fake_utcnow = datetime.datetime(2017, 3, 21, 11, 15, 13, 666666) self.fake_log_time = datetime.datetime(2017, 4, 22, 12, 16, 14, 777777) test_helpers.patch(self, [ 'datetime.datetime', 'clusterfuzz._internal.google_cloud_utils.storage.write_data', ]) self.mock.datetime.utcnow.return_value = self.fake_utcnow def test_upload_to_logs(self): """Test a simple call to upload_to_logs.""" mock_gsutil = mock.MagicMock() self.mock.write_data.return_value = mock_gsutil fuzzer_logs.upload_to_logs('fake-gcs-bucket', 'fake content') self.mock.write_data.assert_called_once_with( 'fake content', 'gs://fake-gcs-bucket/fuzzer_1/fake_job/2017-03-21/11:15:13:666666.log') def test_upload_to_logs_with_all_arguments(self): """Test a call to upload_to_logs with all arguments being passed.""" mock_gsutil = mock.MagicMock() self.mock.write_data.return_value = mock_gsutil fuzzer_logs.upload_to_logs( 'gcs-bucket', 'fake content', time=self.fake_log_time, fuzzer_name='fuzzer_2', job_type='another_job') self.mock.write_data.assert_called_once_with( 'fake content', 'gs://gcs-bucket/fuzzer_2/another_job/2017-04-22/12:16:14:777777.log')

36.636364

80

0.729942

356

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4.761236

0.396067

0.035398

0.042478

0.040118

0.252507

0.189971

0.156932

0

0.044754

0.168321

2,418

65

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0.79811

0.313896

0

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false

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0.27027

0

null

0

null

0

1

0

87aab1ba0bcf39dd2d9b04cea80403f3afe84bdf

2,192

py

Python

test_unitest_fool.py

HeyArtem/python_less_10_HW

addb7d9467acfa14035542cf020482a403af9543

[ "MIT" ]

null

test_unitest_fool.py

HeyArtem/python_less_10_HW

addb7d9467acfa14035542cf020482a403af9543

[ "MIT" ]

null

test_unitest_fool.py

HeyArtem/python_less_10_HW

addb7d9467acfa14035542cf020482a403af9543

[ "MIT" ]

null

import unittest from game_fool import Card from game_fool import Deck class Test_game_fool(unittest.TestCase): def test_1_fool_init(self): ''' Тест1 Создал карту с мастью (suit)=2 и достоинством (rank)=3 Здесь сравниваю suit ''' game_foll_unitest = Card(2,3) self.assertEqual(2, game_foll_unitest.suit) def test_2_fool_rank(self): ''' Тест 2 Создал карту с мастью (suit)=2 и достоинством (rank)=3 Здесь сравниваю rank ''' game_foll_unitest = Card(2, 3) self.assertEqual(3, game_foll_unitest.rank) def test_3_fool_str(self): ''' Тест 3 Тестирую строчное представление карты Создал карту Card(2(масть-Черви), 3(ранк-8)) ''' game_foll_unitest = Card(2, 3) self.assertEqual('8 of Черви ♡', str(game_foll_unitest)) def test_4_fool_init_Deck(self): ''' Тест 4 Тестирую количество карт в созданной колоде Их должно быть 36 ''' game_foll_unitest = Deck() self.assertEqual(36, (len(game_foll_unitest.cards))) def test_5_fool_init_shuffle(self): ''' Тест 5 Тестирую метод shuffle тасую карты в колоде ''' game_foll_unitest = Deck() # создали колоду game_foll_unitest -> колода версия 1 cards_before = game_foll_unitest.cards[:] # создали перемннную cards_before которая равна колода версия 1 (сохранение последовательности добиваемя срезом) swich = False # создали переменную Ложь game_foll_unitest.shuffle() # тасуем колоду cards_after = game_foll_unitest.cards # создали пременную которой присвоено перетасованная колоду версии 1, теперь это колода -> Версия 2 for i,j in zip(cards_before, cards_after): # сравниваем по позициям колоды Версия 1 и версия 2 если есть расхождения, то переменной sich присваиватся True if i != j: swich = True self.assertEqual(True, swich) # Спавниваем два значения True и swich (тоже будет True, если карты перетасованы) if __name__ == '__main__': unittest.main()

31.768116

162

0.639599

282

2,192

4.762411

0.386525

0.077439

0.145197

0.042442

0.202532

0.162323

0.081906

0

0.022959

0.284672

2,192

68

163

32.235294

0.832908

0.394161

0

0.178571

0

0.018018

0

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1

0.178571

false

0

0.107143

0

0.321429

0

null

0

null

0

1

0

87af438c04c9d0f92c9bbe504b18984d32f16dff

13,009

py

Python

causalml/inference/meta/base.py

rainfireliang/causalml

d58024d8de4ab6136c5519949b58a22dd885df29

[ "Apache-2.0" ]

2,919

2019-08-12T23:02:10.000Z

2022-03-31T21:59:34.000Z

causalml/inference/meta/base.py

rainfireliang/causalml

d58024d8de4ab6136c5519949b58a22dd885df29

[ "Apache-2.0" ]

317

2019-08-13T14:16:22.000Z

2022-03-26T08:44:06.000Z

causalml/inference/meta/base.py

rainfireliang/causalml

d58024d8de4ab6136c5519949b58a22dd885df29

[ "Apache-2.0" ]

466

2019-08-18T01:45:14.000Z

2022-03-31T08:11:53.000Z

from abc import ABCMeta, abstractclassmethod import logging import numpy as np import pandas as pd from causalml.inference.meta.explainer import Explainer from causalml.inference.meta.utils import check_p_conditions, convert_pd_to_np from causalml.propensity import compute_propensity_score logger = logging.getLogger('causalml') class BaseLearner(metaclass=ABCMeta): @abstractclassmethod def fit(self, X, treatment, y, p=None): pass @abstractclassmethod def predict(self, X, treatment=None, y=None, p=None, return_components=False, verbose=True): pass def fit_predict(self, X, treatment, y, p=None, return_ci=False, n_bootstraps=1000, bootstrap_size=10000, return_components=False, verbose=True): self.fit(X, treatment, y, p) return self.predict(X, treatment, y, p, return_components, verbose) @abstractclassmethod def estimate_ate(self, X, treatment, y, p=None, bootstrap_ci=False, n_bootstraps=1000, bootstrap_size=10000): pass def bootstrap(self, X, treatment, y, p=None, size=10000): """Runs a single bootstrap. Fits on bootstrapped sample, then predicts on whole population.""" idxs = np.random.choice(np.arange(0, X.shape[0]), size=size) X_b = X[idxs] if p is not None: p_b = {group: _p[idxs] for group, _p in p.items()} else: p_b = None treatment_b = treatment[idxs] y_b = y[idxs] self.fit(X=X_b, treatment=treatment_b, y=y_b, p=p_b) return self.predict(X=X, p=p) @staticmethod def _format_p(p, t_groups): """Format propensity scores into a dictionary of {treatment group: propensity scores}. Args: p (np.ndarray, pd.Series, or dict): propensity scores t_groups (list): treatment group names. Returns: dict of {treatment group: propensity scores} """ check_p_conditions(p, t_groups) if isinstance(p, (np.ndarray, pd.Series)): treatment_name = t_groups[0] p = {treatment_name: convert_pd_to_np(p)} elif isinstance(p, dict): p = {treatment_name: convert_pd_to_np(_p) for treatment_name, _p in p.items()} return p def _set_propensity_models(self, X, treatment, y): """Set self.propensity and self.propensity_models. It trains propensity models for all treatment groups, save them in self.propensity_models, and save propensity scores in self.propensity in dictionaries with treatment groups as keys. It will use self.model_p if available to train propensity models. Otherwise, it will use a default PropensityModel (i.e. ElasticNetPropensityModel). Args: X (np.matrix or np.array or pd.Dataframe): a feature matrix treatment (np.array or pd.Series): a treatment vector y (np.array or pd.Series): an outcome vector """ logger.info('Generating propensity score') p = dict() p_model = dict() for group in self.t_groups: mask = (treatment == group) | (treatment == self.control_name) treatment_filt = treatment[mask] X_filt = X[mask] w_filt = (treatment_filt == group).astype(int) w = (treatment == group).astype(int) propensity_model = self.model_p if hasattr(self, 'model_p') else None p[group], p_model[group] = compute_propensity_score(X=X_filt, treatment=w_filt, p_model=propensity_model, X_pred=X, treatment_pred=w) self.propensity_model = p_model self.propensity = p def get_importance(self, X=None, tau=None, model_tau_feature=None, features=None, method='auto', normalize=True, test_size=0.3, random_state=None): """ Builds a model (using X to predict estimated/actual tau), and then calculates feature importances based on a specified method. Currently supported methods are: - auto (calculates importance based on estimator's default implementation of feature importance; estimator must be tree-based) Note: if none provided, it uses lightgbm's LGBMRegressor as estimator, and "gain" as importance type - permutation (calculates importance based on mean decrease in accuracy when a feature column is permuted; estimator can be any form) Hint: for permutation, downsample data for better performance especially if X.shape[1] is large Args: X (np.matrix or np.array or pd.Dataframe): a feature matrix tau (np.array): a treatment effect vector (estimated/actual) model_tau_feature (sklearn/lightgbm/xgboost model object): an unfitted model object features (np.array): list/array of feature names. If None, an enumerated list will be used method (str): auto, permutation normalize (bool): normalize by sum of importances if method=auto (defaults to True) test_size (float/int): if float, represents the proportion of the dataset to include in the test split. If int, represents the absolute number of test samples (used for estimating permutation importance) random_state (int/RandomState instance/None): random state used in permutation importance estimation """ explainer = Explainer(method=method, control_name=self.control_name, X=X, tau=tau, model_tau=model_tau_feature, features=features, classes=self._classes, normalize=normalize, test_size=test_size, random_state=random_state) return explainer.get_importance() def get_shap_values(self, X=None, model_tau_feature=None, tau=None, features=None): """ Builds a model (using X to predict estimated/actual tau), and then calculates shapley values. Args: X (np.matrix or np.array or pd.Dataframe): a feature matrix tau (np.array): a treatment effect vector (estimated/actual) model_tau_feature (sklearn/lightgbm/xgboost model object): an unfitted model object features (optional, np.array): list/array of feature names. If None, an enumerated list will be used. """ explainer = Explainer(method='shapley', control_name=self.control_name, X=X, tau=tau, model_tau=model_tau_feature, features=features, classes=self._classes) return explainer.get_shap_values() def plot_importance(self, X=None, tau=None, model_tau_feature=None, features=None, method='auto', normalize=True, test_size=0.3, random_state=None): """ Builds a model (using X to predict estimated/actual tau), and then plots feature importances based on a specified method. Currently supported methods are: - auto (calculates importance based on estimator's default implementation of feature importance; estimator must be tree-based) Note: if none provided, it uses lightgbm's LGBMRegressor as estimator, and "gain" as importance type - permutation (calculates importance based on mean decrease in accuracy when a feature column is permuted; estimator can be any form) Hint: for permutation, downsample data for better performance especially if X.shape[1] is large Args: X (np.matrix or np.array or pd.Dataframe): a feature matrix tau (np.array): a treatment effect vector (estimated/actual) model_tau_feature (sklearn/lightgbm/xgboost model object): an unfitted model object features (optional, np.array): list/array of feature names. If None, an enumerated list will be used method (str): auto, permutation normalize (bool): normalize by sum of importances if method=auto (defaults to True) test_size (float/int): if float, represents the proportion of the dataset to include in the test split. If int, represents the absolute number of test samples (used for estimating permutation importance) random_state (int/RandomState instance/None): random state used in permutation importance estimation """ explainer = Explainer(method=method, control_name=self.control_name, X=X, tau=tau, model_tau=model_tau_feature, features=features, classes=self._classes, normalize=normalize, test_size=test_size, random_state=random_state) explainer.plot_importance() def plot_shap_values(self, X=None, tau=None, model_tau_feature=None, features=None, shap_dict=None, **kwargs): """ Plots distribution of shapley values. If shapley values have been pre-computed, pass it through the shap_dict parameter. If shap_dict is not provided, this builds a new model (using X to predict estimated/actual tau), and then calculates shapley values. Args: X (np.matrix or np.array or pd.Dataframe): a feature matrix. Required if shap_dict is None. tau (np.array): a treatment effect vector (estimated/actual) model_tau_feature (sklearn/lightgbm/xgboost model object): an unfitted model object features (optional, np.array): list/array of feature names. If None, an enumerated list will be used. shap_dict (optional, dict): a dict of shapley value matrices. If None, shap_dict will be computed. """ override_checks = False if shap_dict is None else True explainer = Explainer(method='shapley', control_name=self.control_name, X=X, tau=tau, model_tau=model_tau_feature, features=features, override_checks=override_checks, classes=self._classes) explainer.plot_shap_values(shap_dict=shap_dict) def plot_shap_dependence(self, treatment_group, feature_idx, X, tau, model_tau_feature=None, features=None, shap_dict=None, interaction_idx='auto', **kwargs): """ Plots dependency of shapley values for a specified feature, colored by an interaction feature. If shapley values have been pre-computed, pass it through the shap_dict parameter. If shap_dict is not provided, this builds a new model (using X to predict estimated/actual tau), and then calculates shapley values. This plots the value of the feature on the x-axis and the SHAP value of the same feature on the y-axis. This shows how the model depends on the given feature, and is like a richer extension of the classical partial dependence plots. Vertical dispersion of the data points represents interaction effects. Args: treatment_group (str or int): name of treatment group to create dependency plot on feature_idx (str or int): feature index / name to create dependency plot on X (np.matrix or np.array or pd.Dataframe): a feature matrix tau (np.array): a treatment effect vector (estimated/actual) model_tau_feature (sklearn/lightgbm/xgboost model object): an unfitted model object features (optional, np.array): list/array of feature names. If None, an enumerated list will be used. shap_dict (optional, dict): a dict of shapley value matrices. If None, shap_dict will be computed. interaction_idx (optional, str or int): feature index / name used in coloring scheme as interaction feature. If "auto" then shap.common.approximate_interactions is used to pick what seems to be the strongest interaction (note that to find to true strongest interaction you need to compute the SHAP interaction values). """ override_checks = False if shap_dict is None else True explainer = Explainer(method='shapley', control_name=self.control_name, X=X, tau=tau, model_tau=model_tau_feature, features=features, override_checks=override_checks, classes=self._classes) explainer.plot_shap_dependence(treatment_group=treatment_group, feature_idx=feature_idx, shap_dict=shap_dict, interaction_idx=interaction_idx, **kwargs)

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0.305263

0

null

0

null

0

1

0

87b274375a58a74d68ee6f91801f5f5d232a7bb2

16,887

py

Python

archive/preprocess.py

whashi44/uscis-analysis

975402c77cb8e74654f2568117c08af190116ce1

[ "MIT" ]

null

archive/preprocess.py

whashi44/uscis-analysis

975402c77cb8e74654f2568117c08af190116ce1

[ "MIT" ]

null

archive/preprocess.py

whashi44/uscis-analysis

975402c77cb8e74654f2568117c08af190116ce1

[ "MIT" ]

null

""" Preprocess the uscis I485 Adjustment of status data 1. Download the csv files from the website 2. Rename the file and omit unnecessary files 3. Extract information from csv and save it into csv file Currently works from 2014 qtr 1 through 2019 qtr 2 """ # standard library from os.path import basename import os from urllib.parse import urljoin import re import shutil import csv # External library import requests from bs4 import BeautifulSoup from natsort import natsorted import numpy as np import pandas as pd from fiscalyear import FiscalQuarter def main(): data_path = "data" raw_path = "raw" # download(raw_path) # rename(data_path, raw_path) data, header = extract(data_path) modify(data, header) # ----------------------------------------------------------------------------------------------------- def download(path="raw"): """ Automatically download all the csv file from uscis website with specified url """ try: print(f"Making folder: {path}") os.mkdir(path) except FileExistsError: raise # finally: # os.chdir(path) # print(f"Changed directory to: {path}") base = "https://www.uscis.gov/tools/reports-studies/immigration-forms-data?topic_id=20658&field_native_doc_issue_date_value%5Bvalue%5D%5Bmonth%5D=&field_native_doc_issue_date_value_1%5Bvalue%5D%5Byear%5D=&combined=&items_per_page=100" with requests.Session() as s: # stream will make it generator and parse faster url = s.get(base, stream=True).text # Specifying the parse is necessary to avoid warning soup = BeautifulSoup(url, "html.parser") # creating generator with all the url that end with .csv # the soup.select will include other unnecessary html parameter, hence urljoin is used to extract just the href for link in (urljoin("", a["href"]) for a in soup.select("a[href$='.csv']")): # Since the link still has value such as "https://....", basename() is used to extract only the csv file name to open file = basename(link) # Creating a path to raw folder to save file file_path = f"{path}/{file}" with open(file_path, "wb") as write_file: print(f"saving .csv file:{link} to path") write_file.write(requests.get(link).content) # ----------------------------------------------------------------------------------------------------- def rename(data_path="data", raw_path="raw"): """ Rename files because the original file name had inconsistency Remove other files that is not useful for analysis (i.e. one with inconsistent format, especially before 2014) Remove fy 2013 csv because the csv format was completely different. """ # data_path = "data" # raw_path = "raw" # make data deposit directory try: print(f"Making folder: {data_path}") os.mkdir(data_path) except FileExistsError: raise # finally: # # Change directory to raw so that raw files can be retrieved # os.chdir(raw_path) # print(f"Changed directory to: {raw_path}") # Get all the files from raw folder all_files = os.listdir(raw_path) # Extracting useful files files = [file for file in all_files if "fy" in file] # Extracting unuseful files remove_files = [file for file in all_files if "fy" not in file] years = [] quarters = [] # Find year and quarter information from file name ex. "I485_data_fy2014_qtr3.csv" for file in files: numbers = re.findall( r"\d+", file # for number ) # 0th is I485, 1st is fiscal year, 2nd is quarter years.append(numbers[1]) quarters.append(numbers[2]) # Rename files to uniform format, and save copy to "data" folder for file, year, quarter in zip(files, years, quarters): print(f"Copying and renaming the filename from: \n{file}") new_name = f"I485_data_fy{year}_qtr{quarter}.csv" shutil.copyfile(f"{raw_path}/{file}", f"{data_path}/{new_name}") print(f"To: {new_name}") # remove special file, which has inconsistent format print(f"Removing special file, the 2013 quarter 3, due to its inconsistent format") os.remove(f"{data_path}/I485_data_fy2013_qtr3.csv") # ----------------------------------------------------------------------------------------------------- def extract(data_path="data"): """Read csv file from data folder and perform following: 1. Extract header information from the first file, assuming all other file have same/similar header information 2. Extract state and city application information 3. Convert them to numpy array and return """ # # Change directory to data folder # try: # os.chdir("data") # except FileNotFoundError: # raise FileNotFoundError # Grab all the files so you can iterate through files = os.listdir(data_path) # I want to use the newest report to extract the basic header because it has the most information. files = natsorted(files, reverse=True) # Grab header information with open(f"{data_path}/{files[0]}", "r") as read_file: # read file as csv, it automatically skip comma if there is quote csv_file = csv.reader(read_file, delimiter=",") header = [] for row in csv_file: # removing leading and trailing white space row = list(map(str.strip, row)) # lower casing all the item to avoid word mismatch row = list(map(str.lower, row)) # for special case, 2019 qtr 1 and qtr 2 other_count = 1 # Looking at the file structure, there is a category for green card application, # As well as the result of application for each category, hence concatnate would be # appropriate to increase consistency and uniformity # pre-2017qtr1 has family-based1, instead of family-based if "family-based" in row or "family-based1" in row: category_name = "" # for "family-based", "employment" categories = row # for green card category results = next(csv_file) # for application status found_family = False # flag found_other = False # For concatinating the category and result for category, status in zip(categories, results): # Checking the condition, if category name appears, store the category name # If not, then use the previous category name # Then, cancatnate the category name and the status with ":" # For family based green card if "family" in category: category_name = "Family" # family should come first, hence the flag is true found_family = True # For employment based green card elif "employment" in category: category_name = "Employment" # For humanitarian based green card elif "humanitarian" in category: category_name = "Humanitarian" # For other category elif "other" in category: category_name = "Other" found_other = True # For total count of application elif "total" in category: category_name = "Total" # For 2019 qtr 1 and qtr 2 with shifted "total" elif other_count == 4: category_name = "Total" # For keeping track of "other" to make sure "total" is included elif found_other: other_count += 1 # For first couple empty cases elif not found_family: pass # There are some numbers after the result (i.e. Application2) so strip those status = "".join(i for i in status if not i.isdigit()) # concatnate to create better category value = category_name + ":" + status header.append(value) # Fill those empty header header[0:3] = ["State", "City", "Abbreviation"] # eliminate those empty strings in the end header = header[0:23] # Add year and quarter header.append("Year") header.append("Quarter") # Grab states and city information city_cases = [] for file in files: print(f"working on file:{file}") # Find the year and quarter from the file name numbers = re.findall( r"\d+", file # for number ) # 0th is I485, 1st is fiscal year, 2nd is quarter year = numbers[1] quarter = numbers[2] with open(f"{data_path}/{file}", "r") as read_file: # csv_file is a list of list csv_file = csv.reader(read_file, delimiter=",") # Each row is a list for row in csv_file: # removing leading and trailing white space row = list(map(str.strip, row)) # lower casing for case-insensitive comparison row = list(map(str.lower, row)) # If it finds the section of the total number of case, store that if "total" in row[0]: total_case_numbers = row # For looping through states # if the first column(state) is alabama, or alaska (before 2017_qtr3) if row[0] == "alabama" or row[0] == "alaska": # Loop until final city, vermont while row[1] != "vermont": # if 1st column is not empty, meaning this row is state if row[0] != "": # grab the state name state_name = row[0].title() # For special case in 2017, 1st quarter for guam, the row is shifted so we need to grab it now if ( (year == "2017") and (quarter == "1") and (state_name == "Guam") ): # Grab current line because it has all the information row_with_state = row row_with_state[0] = state_name # check the next line, which has the city name row = next(csv_file) # removing leading and trailing white space row = list(map(str.strip, row)) # create lower case row = list(map(str.lower, row)) # Grab city name row_with_state[1] = row[1].title() # capitalize state abbreviation row_with_state[2] = row[2].upper() row_with_state[23] = year row_with_state[24] = quarter # if the 1st column is empty, meaning this row is city # some year has repeating the header at the middle of the line, hence 2nd if statement is counter for that (see 2018 qtr 1 Kentucky) elif row[0] == "" and row[1] != "": row_with_state = row # adding the state name to the initial part row_with_state[0] = state_name # initialize the city name row_with_state[1] = row_with_state[1].title() # capitalize state abbreviation row_with_state[2] = row_with_state[2].upper() # Some year has empty strings in the end of the row, hence simply substitute try: row_with_state[23] = year row_with_state[24] = quarter # Some states do not have empty strings in the of the row, hence handle that except IndexError: # for pre 2014, there is no city abbreviation, insert empty string to avoid index error later on if year == "2014": row_with_state.insert(2, "") # other case append instead of inject row_with_state.append(year) row_with_state.append(quarter) # Add to state list city_cases.append(row_with_state) # keep checking the next row row = next(csv_file) # removing leading and trailing white space row = list(map(str.strip, row)) row = list(map(str.lower, row)) # create lower case # convert the list of list to array of array city_cases = np.array([np.array(x)[0:25] for x in city_cases]) # print(np.shape(city_cases)) # convert the list to array header = np.array(header) print("Converting empty strings, 'd' and 'D' to NaN") # Convert the empty strings and d or D to NaN city_cases[city_cases == ""] = np.NaN city_cases[city_cases == "d"] = np.NaN city_cases[city_cases == "D"] = np.NaN # Convert the hyphen to 0 print("Converting '-' to 0") city_cases[city_cases == "-"] = 0 return city_cases, header def modify(data, header): """Take the data and convert to pandas data frame""" # Dataframe for easier manipulation df_original = pd.DataFrame(data=data, columns=header) # We don't need abbreviation for cities, so drop those df_original = df_original.drop(columns="Abbreviation") # Currently, there are excess columns, so simplify by adding category row # So there will be only columns for State,city,received,approved,denied,pending,category # First, let's slice the data frame into location(state & city), family, employment, humanitarian, other, total, and time (year & quarter) # Create a copy to avoid settingwithcopywarning location = df_original.iloc[:, 0:2].copy() family = df_original.iloc[:, 2:6].copy() employment = df_original.iloc[:, 6:10].copy() humanitarian = df_original.iloc[:, 10:14].copy() other = df_original.iloc[:, 14:18].copy() total = df_original.iloc[:, 18:22].copy() time = df_original.iloc[:, 22:24].copy() # Using fiscal year package, we can identify the date and year # start attribute indicate the start of the fiscal quarter, date() is simply to return only dates, not time time["Start_date"] = time.apply( lambda row: FiscalQuarter(row.Year, row.Quarter).start.date(), axis=1 ) time["End_date"] = time.apply( lambda row: FiscalQuarter(row.Year, row.Quarter).end.date(), axis=1 ) # Store all data frame into list to loop through all_df = [family, employment, humanitarian, other, total] # Let's rename the columns name, and then put "category" column with corresponding names category_list = ["Family", "Employment", "Humanitarian", "Other", "Total"] # I also want to change the name of the columns new_names = ["Received", "Approved", "Denied", "Pending"] for i, df in enumerate(all_df): # Grab column names col_names = list(df.columns.values) # Create dictionary key = col names, value = new_names name_dict = dict(zip(col_names, new_names)) # Rename the columns i.e. Family:application_received -> Received df = df.rename(columns=name_dict) # Keep the category i.e. Family, employment df["Category"] = category_list[i] # concatinate location(state,city) this data frame, and time df = pd.concat([location, df, time], axis=1, sort=False) all_df[i] = df df_final = pd.concat(all_df) # There is annoying comma in the number, so remove those for col in new_names: df_final[col] = df_final[col].str.replace(",", "") save_file = "I485_data_all.csv" # save to csv file, without the index name df_final.to_csv(save_file, index=False) print(f"saved to {save_file}") # print(df_final) if __name__ == "__main__": main()

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238

0.561142

2,076

16,887

4.473988

0.214836

0.013566

0.023256

0.011197

0.177864

0.138351

0.102175

0.089901

0.077735

0

0.018608

0.341268

16,887

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false

0.005208

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0.09375

0.052083

0

null

0

null

0

1

0

87b44680c68879d718bb0507b7b071f19f8ece93

4,709

py

Python

mdbuild.py

c0d3z3r0/mdBuild

b1b2e3cf90bc3d3636b8d1e35babf75ed2f5409a

[ "MIT" ]

null

mdbuild.py

c0d3z3r0/mdBuild

b1b2e3cf90bc3d3636b8d1e35babf75ed2f5409a

[ "MIT" ]

null

mdbuild.py

c0d3z3r0/mdBuild

b1b2e3cf90bc3d3636b8d1e35babf75ed2f5409a

[ "MIT" ]

null

#!/usr/bin/env python3 __author__ = 'Michael Niewoehner <c0d3z3r0>' __email__ = 'mniewoeh@stud.hs-offenburg.de' import os import sys import re import base64 import subprocess import argparse def checkDependencies(): dep = ['hoedown', 'wkhtmltopdf'] missing = [] for d in dep: if subprocess.getstatusoutput('which ' + d)[0]: missing.append(d) if missing: print("Please install missing dependencies: " + ', '.join(missing)) sys.exit(1) def readFileToList(file): f = open(file, 'r') return f.readlines() def file2base64(file): try: f = open(file, 'rb') return base64.b64encode(f.read()).decode() except FileNotFoundError: return "" def writeListToFile(file, lines): f = open(file, 'w') f.writelines(lines) f.close() def readStyles(styles): st = [] for style in styles: s = readFileToList(style) s.insert(0, '<style type="text/css">\n') s.append('</style>\n') st.extend(s) return st def getHeader(): header = [] header_html = """\ <!DOCTYPE html><html> <head> <meta charset="utf-8"> <title>%s</title> </head> <body>""" % out_fname header.extend(header_html.splitlines(keepends=True)) styles = readStyles([ 'style/GitHub2.css', 'style/prism.css', 'style/custom.css' ]) for s in styles: header.insert(-2, s) return header def getFooter(): footer = [] footer.append('<script type="text/javascript">\n') footer.extend(readFileToList('style/prism.js')) footer.extend("""\ </script> </body> </html>""".splitlines(keepends=True)) return footer def markdown2Html(file): md = ['\n\n'] md.extend(subprocess.getoutput( 'hoedown --all-block --all-flags --all-negative --all-span %s' % file ).splitlines(keepends=True)) for m in md: if args.docs.index(file) == 0 and '<h1>' in m: md[md.index(m)] = re.sub( '<h1>', '<h1 style=\'page-break-before: avoid;\'>', m) if 'img src' in m: src = re.search('src="(.*?)"', m).group(1) ext = re.search('(?<=\.).{1,4}?$', src).group(0) b64 = file2base64(os.path.dirname(file) + '/' + src) newimg = 'data:image/' + ext + ';base64,' + b64 md[md.index(m)] = re.sub(src, newimg, m) if 'language-sh' in m: md[md.index(m)] = re.sub('language-sh', 'language-bash', m) return md def html2pdf(input, title, output): subprocess.getoutput( 'wkhtmltopdf --dpi 150 --print-media-type --title ' + ' '.join([title, input, output]) ) def main(): checkDependencies() viewer = { 'linux': {'pdf': 'evince', 'html': 'firefox'}, 'darwin': {'pdf': 'open', 'html': 'open'}}[sys.platform] output = [] output.extend(getHeader()) for doc in args.docs: output.extend(markdown2Html(doc)) output.extend(getFooter()) if not args.html and not args.both: writeListToFile('/tmp/%s.html' % out_fname, output) html2pdf('/tmp/%s.html' % out_fname, out_fname, '%s.pdf' % out_fname) if not args.no_open: subprocess.Popen('%s %s.pdf' % (viewer['pdf'], out_fname), shell=True) if args.both: writeListToFile('%s.html' % out_fname, output) html2pdf('%s.html' % out_fname, out_fname, '%s.pdf' % out_fname) if not args.no_open: subprocess.Popen('%s %s.html' % (viewer['html'], out_fname), shell=True) subprocess.Popen('%s %s.pdf' % (viewer['pdf'], out_fname), shell=True) elif args.html: writeListToFile('%s.html' % out_fname, output) if not args.no_open: subprocess.Popen('%s %s.html' % (viewer['html'], out_fname), shell=True) if __name__ == '__main__': parser = argparse.ArgumentParser(description='mdbuild') htmlpdf= parser.add_mutually_exclusive_group() htmlpdf.add_argument('-t', '--html', action='store_true', help='create html only') htmlpdf.add_argument('-b', '--both', action='store_true', help='create pdf and html') parser.add_argument('-o', '--output', help='output filename') parser.add_argument('-n', '--no-open', action='store_true', help='do not open file after build') parser.add_argument('docs', nargs='+', help='documents to include') args = parser.parse_args() if args.output: out_fname = args.output else: out_fname = re.search('[^\.]+', args.docs[0]).group(0) main()

27.7

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0.5653

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0.319298

0.048986

0.032147

0.024876

0.196326

0.173364

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4,709

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0

null

0

null

0

1

0

87b601d038e77ca7cab9f2c9d3e4980c8347af3b

1,416

py

Python

test/test_fd.py

Xiang-cd/realsafe

39f632e950562fa00ac26d34d13b2691c9c5f013

[ "MIT" ]

2

2021-01-27T06:14:50.000Z

2021-10-30T08:23:48.000Z

test/test_fd.py

Xiang-cd/realsafe

39f632e950562fa00ac26d34d13b2691c9c5f013

[ "MIT" ]

2

2021-08-25T16:14:37.000Z

2022-02-10T02:26:07.000Z

test/test_fd.py

Xiang-cd/realsafe

39f632e950562fa00ac26d34d13b2691c9c5f013

[ "MIT" ]

1

2022-01-05T04:36:22.000Z

#!/usr/bin/env python3 import os import tensorflow as tf import numpy as np from realsafe import CrossEntropyLoss, BIM from realsafe.model.loader import load_model_from_path from realsafe.dataset import imagenet, dataset_to_iterator batch_size = 25 session = tf.Session() model_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), '../example/imagenet/resnet152_fd.py') rs_model = load_model_from_path(model_path) model = rs_model.load(session) xs_ph = tf.placeholder(model.x_dtype, shape=(batch_size, *model.x_shape)) lgs, lbs = model.logits_and_labels(xs_ph) dataset = imagenet.load_dataset_for_classifier(model, load_target=True) dataset = dataset.batch(batch_size).take(10) loss = CrossEntropyLoss(model) attack = BIM( model=model, batch_size=batch_size, loss=loss, goal='ut', distance_metric='l_inf', session=session ) attack.config( iteration=50, magnitude=8.0 / 255.0, alpha=0.5 / 255.0, ) accs, adv_accs = [], [] for filenames, xs, ys, ys_target in dataset_to_iterator(dataset, session): xs_adv = attack.batch_attack(xs, ys=ys) lbs_pred = session.run(lbs, feed_dict={xs_ph: xs}) lbs_adv = session.run(lbs, feed_dict={xs_ph: xs_adv}) accs.append(np.equal(ys, lbs_pred).astype(np.float).mean()) adv_accs.append(np.equal(ys, lbs_adv).astype(np.float).mean()) print(accs[-1], adv_accs[-1]) print(np.mean(accs), np.mean(adv_accs))

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108

0.731638

226

1,416

4.353982

0.380531

0.045732

0.026423

0.034553

0.105691

0.054878

0

0.019512

0.131356

1,416

51

109

27.764706

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0

null

0

null

0

1

0

87b623971f6ce4c2d43cab30e8be7cf30931d68c

3,692

py

Python

Sketches/JMB/mysite/settings.py

sparkslabs/kamaelia_orig

24b5f855a63421a1f7c6c7a35a7f4629ed955316

[ "Apache-2.0" ]

12

2015-10-20T10:22:01.000Z

2021-07-19T10:09:44.000Z

Sketches/JMB/mysite/settings.py

sparkslabs/kamaelia_orig

24b5f855a63421a1f7c6c7a35a7f4629ed955316

[ "Apache-2.0" ]

2

2015-10-20T10:22:55.000Z

2017-02-13T11:05:25.000Z

Sketches/JMB/mysite/settings.py

sparkslabs/kamaelia_orig

24b5f855a63421a1f7c6c7a35a7f4629ed955316

[ "Apache-2.0" ]

6

2015-03-09T12:51:59.000Z

2020-03-01T13:06:21.000Z

# -*- coding: utf-8 -*- # Copyright 2010 British Broadcasting Corporation and Kamaelia Contributors(1) # # (1) Kamaelia Contributors are listed in the AUTHORS file and at # http://www.kamaelia.org/AUTHORS - please extend this file, # not this notice. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # Django settings for mysite project. DEBUG = True TEMPLATE_DEBUG = DEBUG ADMINS = ( # ('Your Name', 'your_email@domain.com'), ) MANAGERS = ADMINS DATABASE_ENGINE = 'sqlite3' # 'postgresql_psycopg2', 'postgresql', 'mysql', 'sqlite3' or 'ado_mssql'. DATABASE_NAME = '/home/jason/mysite/mysite.db' # Or path to database file if using sqlite3. DATABASE_USER = '' # Not used with sqlite3. DATABASE_PASSWORD = '' # Not used with sqlite3. DATABASE_HOST = '' # Set to empty string for localhost. Not used with sqlite3. DATABASE_PORT = '' # Set to empty string for default. Not used with sqlite3. # Local time zone for this installation. Choices can be found here: # http://www.postgresql.org/docs/8.1/static/datetime-keywords.html#DATETIME-TIMEZONE-SET-TABLE # although not all variations may be possible on all operating systems. # If running in a Windows environment this must be set to the same as your # system time zone. TIME_ZONE = 'America/Chicago' # Language code for this installation. All choices can be found here: # http://www.w3.org/TR/REC-html40/struct/dirlang.html#langcodes # http://blogs.law.harvard.edu/tech/stories/storyReader$15 LANGUAGE_CODE = 'en-us' SITE_ID = 1 # If you set this to False, Django will make some optimizations so as not # to load the internationalization machinery. USE_I18N = True # Absolute path to the directory that holds media. # Example: "/home/media/media.lawrence.com/" MEDIA_ROOT = '' # URL that handles the media served from MEDIA_ROOT. # Example: "http://media.lawrence.com" MEDIA_URL = '' # URL prefix for admin media -- CSS, JavaScript and images. Make sure to use a # trailing slash. # Examples: "http://foo.com/media/", "/media/". ADMIN_MEDIA_PREFIX = '/media/' # Make this unique, and don't share it with anybody. SECRET_KEY = 'bbfgxp&2+t&=yo!0@wey-_n4fcxhx8gdllmp%1s#%z85w_opv5' # List of callables that know how to import templates from various sources. TEMPLATE_LOADERS = ( 'django.template.loaders.filesystem.load_template_source', 'django.template.loaders.app_directories.load_template_source', # 'django.template.loaders.eggs.load_template_source', ) MIDDLEWARE_CLASSES = ( 'django.middleware.common.CommonMiddleware', 'django.contrib.sessions.middleware.SessionMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.middleware.doc.XViewMiddleware', ) ROOT_URLCONF = 'mysite.urls' TEMPLATE_DIRS = ( # Put strings here, like "/home/html/django_templates" or "C:/www/django/templates". # Always use forward slashes, even on Windows. # Don't forget to use absolute paths, not relative paths. ) INSTALLED_APPS = ( 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.sites', "django.contrib.admin", 'mysite.polls' )

36.196078

111

0.728061

512

3,692

5.181641

0.496094

0.034301

0.016585

0.027139

0.094233

0.050509

0.021108

0

0.012033

0.167118

3,692

101

112

36.554455

0.850732

0.656826

0

0.448105

0.352554

0

1

0

false

0.025

0

null

0

null

0

1

0

87b8637f693d0b8229b1b49f56096e77a5c26a9c

5,584

py

Python

mdns/Phidget22Python/Phidget22/Devices/IR.py

rabarar/phidget_docker

ceca56c86d27f291a4300a1257c02096862335ec

[ "MIT" ]

null

mdns/Phidget22Python/Phidget22/Devices/IR.py

rabarar/phidget_docker

ceca56c86d27f291a4300a1257c02096862335ec

[ "MIT" ]

null

mdns/Phidget22Python/Phidget22/Devices/IR.py

rabarar/phidget_docker

ceca56c86d27f291a4300a1257c02096862335ec

[ "MIT" ]

null

import sys import ctypes from Phidget22.PhidgetSupport import PhidgetSupport from Phidget22.Async import * from Phidget22.CodeInfo import CodeInfo from Phidget22.IRCodeEncoding import IRCodeEncoding from Phidget22.IRCodeLength import IRCodeLength from Phidget22.PhidgetException import PhidgetException from Phidget22.Phidget import Phidget class IR(Phidget): def __init__(self): Phidget.__init__(self) self.handle = ctypes.c_void_p() if sys.platform == 'win32': self._CodeFactory = ctypes.WINFUNCTYPE(None, ctypes.c_void_p, ctypes.c_void_p, ctypes.c_char_p, ctypes.c_uint32, ctypes.c_int) else: self._CodeFactory = ctypes.CFUNCTYPE(None, ctypes.c_void_p, ctypes.c_void_p, ctypes.c_char_p, ctypes.c_uint32, ctypes.c_int) self._Code = None self._onCode = None if sys.platform == 'win32': self._LearnFactory = ctypes.WINFUNCTYPE(None, ctypes.c_void_p, ctypes.c_void_p, ctypes.c_char_p, ctypes.POINTER(CodeInfo)) else: self._LearnFactory = ctypes.CFUNCTYPE(None, ctypes.c_void_p, ctypes.c_void_p, ctypes.c_char_p, ctypes.POINTER(CodeInfo)) self._Learn = None self._onLearn = None if sys.platform == 'win32': self._RawDataFactory = ctypes.WINFUNCTYPE(None, ctypes.c_void_p, ctypes.c_void_p, ctypes.POINTER(ctypes.c_int32), ctypes.c_int32) else: self._RawDataFactory = ctypes.CFUNCTYPE(None, ctypes.c_void_p, ctypes.c_void_p, ctypes.POINTER(ctypes.c_int32), ctypes.c_int32) self._RawData = None self._onRawData = None __func = PhidgetSupport.getDll().PhidgetIR_create __func.restype = ctypes.c_int32 res = __func(ctypes.byref(self.handle)) if res > 0: raise PhidgetException(res) def __del__(self): Phidget.__del__(self) def _localCodeEvent(self, handle, userPtr, code, bitCount, isRepeat): if self._Code == None: return code = code.decode('utf-8') self._Code(self, code, bitCount, isRepeat) def setOnCodeHandler(self, handler): if handler == None: self._Code = None self._onCode = None else: self._Code = handler self._onCode = self._CodeFactory(self._localCodeEvent) try: __func = PhidgetSupport.getDll().PhidgetIR_setOnCodeHandler __func.restype = ctypes.c_int32 res = __func(self.handle, self._onCode, None) except RuntimeError: self._Code = None self._onCode = None def _localLearnEvent(self, handle, userPtr, code, codeInfo): if self._Learn == None: return code = code.decode('utf-8') if codeInfo != None: codeInfo = codeInfo.contents codeInfo.toPython() self._Learn(self, code, codeInfo) def setOnLearnHandler(self, handler): if handler == None: self._Learn = None self._onLearn = None else: self._Learn = handler self._onLearn = self._LearnFactory(self._localLearnEvent) try: __func = PhidgetSupport.getDll().PhidgetIR_setOnLearnHandler __func.restype = ctypes.c_int32 res = __func(self.handle, self._onLearn, None) except RuntimeError: self._Learn = None self._onLearn = None def _localRawDataEvent(self, handle, userPtr, data, dataLen): if self._RawData == None: return data = [data[i] for i in range(dataLen)] self._RawData(self, data) def setOnRawDataHandler(self, handler): if handler == None: self._RawData = None self._onRawData = None else: self._RawData = handler self._onRawData = self._RawDataFactory(self._localRawDataEvent) try: __func = PhidgetSupport.getDll().PhidgetIR_setOnRawDataHandler __func.restype = ctypes.c_int32 res = __func(self.handle, self._onRawData, None) except RuntimeError: self._RawData = None self._onRawData = None def getLastCode(self): _code = (ctypes.c_char * 33)() _codeLen = ctypes.c_int32(33) _bitCount = ctypes.c_uint32() __func = PhidgetSupport.getDll().PhidgetIR_getLastCode __func.restype = ctypes.c_int32 result = __func(self.handle, ctypes.byref(_code), _codeLen, ctypes.byref(_bitCount)) if result > 0: raise PhidgetException(result) return _code.value.decode('utf-8'), _bitCount.value def getLastLearnedCode(self): _code = (ctypes.c_char * 33)() _codeLen = ctypes.c_int32(33) _codeInfo = CodeInfo() __func = PhidgetSupport.getDll().PhidgetIR_getLastLearnedCode __func.restype = ctypes.c_int32 result = __func(self.handle, ctypes.byref(_code), _codeLen, ctypes.byref(_codeInfo)) if result > 0: raise PhidgetException(result) return _code.value.decode('utf-8'), _codeInfo.toPython() def transmit(self, code, codeInfo): _code = ctypes.create_string_buffer(code.encode('utf-8')) _codeInfo = codeInfo.fromPython() __func = PhidgetSupport.getDll().PhidgetIR_transmit __func.restype = ctypes.c_int32 result = __func(self.handle, ctypes.byref(_code), ctypes.byref(_codeInfo)) if result > 0: raise PhidgetException(result) def transmitRaw(self, data, carrierFrequency, dutyCycle, gap): _data = (ctypes.c_uint32 * len(data))(*data) _dataLen = ctypes.c_int32(len(data)) _carrierFrequency = ctypes.c_uint32(carrierFrequency) _dutyCycle = ctypes.c_double(dutyCycle) _gap = ctypes.c_uint32(gap) __func = PhidgetSupport.getDll().PhidgetIR_transmitRaw __func.restype = ctypes.c_int32 result = __func(self.handle, ctypes.byref(_data), _dataLen, _carrierFrequency, _dutyCycle, _gap) if result > 0: raise PhidgetException(result) def transmitRepeat(self): __func = PhidgetSupport.getDll().PhidgetIR_transmitRepeat __func.restype = ctypes.c_int32 result = __func(self.handle) if result > 0: raise PhidgetException(result) RAW_DATA_LONG_SPACE = 4294967295 IR_MAX_CODE_BIT_COUNT = 128 IR_MAX_CODE_STR_LENGTH = 33

29.860963

132

0.744807

714

5,584

5.501401

0.147059

0.078411

0.04888

0.039715

0.470978

0.437882

0.333503

0.300917

0.273931

0

0.020584

0.147385

5,584

186

133

30.021505

0.804453

0

0.438356

0

0.007163

0

1

0.089041

false

0

0.061644

0

0.212329

0

null

0

null

0

1

0

87b8e5f7f61ff1ffcca34bab7f115ef927c9aaf8

6,016

py

Python

mux_python/models/metric.py

moaazsidat/mux-python

3f03b9dd0761fa1a0cd5bdbeac85ccf4f326508c

[ "MIT" ]

36

2019-02-28T21:18:39.000Z

2022-03-04T19:58:45.000Z

mux_python/models/metric.py

moaazsidat/mux-python

3f03b9dd0761fa1a0cd5bdbeac85ccf4f326508c

[ "MIT" ]

7

2019-04-01T14:48:34.000Z

2022-03-04T16:31:34.000Z

mux_python/models/metric.py

moaazsidat/mux-python

3f03b9dd0761fa1a0cd5bdbeac85ccf4f326508c

[ "MIT" ]

9

2019-11-29T03:57:58.000Z

2022-03-02T17:29:25.000Z

# coding: utf-8 """ Mux API Mux is how developers build online video. This API encompasses both Mux Video and Mux Data functionality to help you build your video-related projects better and faster than ever before. # noqa: E501 The version of the OpenAPI document: v1 Contact: devex@mux.com Generated by: https://openapi-generator.tech """ import inspect import pprint import re # noqa: F401 import six from mux_python.configuration import Configuration class Metric(object): """NOTE: This class is auto generated by OpenAPI Generator. Ref: https://openapi-generator.tech Do not edit the class manually. """ """ Attributes: openapi_types (dict): The key is attribute name and the value is attribute type. attribute_map (dict): The key is attribute name and the value is json key in definition. """ openapi_types = { 'value': 'float', 'type': 'str', 'name': 'str', 'metric': 'str', 'measurement': 'str' } attribute_map = { 'value': 'value', 'type': 'type', 'name': 'name', 'metric': 'metric', 'measurement': 'measurement' } def __init__(self, value=None, type=None, name=None, metric=None, measurement=None, local_vars_configuration=None): # noqa: E501 """Metric - a model defined in OpenAPI""" # noqa: E501 if local_vars_configuration is None: local_vars_configuration = Configuration.get_default_copy() self.local_vars_configuration = local_vars_configuration self._value = None self._type = None self._name = None self._metric = None self._measurement = None self.discriminator = None if value is not None: self.value = value if type is not None: self.type = type if name is not None: self.name = name if metric is not None: self.metric = metric if measurement is not None: self.measurement = measurement @property def value(self): """Gets the value of this Metric. # noqa: E501 :return: The value of this Metric. # noqa: E501 :rtype: float """ return self._value @value.setter def value(self, value): """Sets the value of this Metric. :param value: The value of this Metric. # noqa: E501 :type value: float """ self._value = value @property def type(self): """Gets the type of this Metric. # noqa: E501 :return: The type of this Metric. # noqa: E501 :rtype: str """ return self._type @type.setter def type(self, type): """Sets the type of this Metric. :param type: The type of this Metric. # noqa: E501 :type type: str """ self._type = type @property def name(self): """Gets the name of this Metric. # noqa: E501 :return: The name of this Metric. # noqa: E501 :rtype: str """ return self._name @name.setter def name(self, name): """Sets the name of this Metric. :param name: The name of this Metric. # noqa: E501 :type name: str """ self._name = name @property def metric(self): """Gets the metric of this Metric. # noqa: E501 :return: The metric of this Metric. # noqa: E501 :rtype: str """ return self._metric @metric.setter def metric(self, metric): """Sets the metric of this Metric. :param metric: The metric of this Metric. # noqa: E501 :type metric: str """ self._metric = metric @property def measurement(self): """Gets the measurement of this Metric. # noqa: E501 :return: The measurement of this Metric. # noqa: E501 :rtype: str """ return self._measurement @measurement.setter def measurement(self, measurement): """Sets the measurement of this Metric. :param measurement: The measurement of this Metric. # noqa: E501 :type measurement: str """ self._measurement = measurement def to_dict(self, serialize=False): """Returns the model properties as a dict""" result = {} def convert(x): if hasattr(x, "to_dict"): args = inspect.getargspec(x.to_dict).args if len(args) == 1: return x.to_dict() else: return x.to_dict(serialize) else: return x for attr, _ in six.iteritems(self.openapi_types): value = getattr(self, attr) attr = self.attribute_map.get(attr, attr) if serialize else attr if isinstance(value, list): result[attr] = list(map( lambda x: convert(x), value )) elif isinstance(value, dict): result[attr] = dict(map( lambda item: (item[0], convert(item[1])), value.items() )) else: result[attr] = convert(value) return result def to_str(self): """Returns the string representation of the model""" return pprint.pformat(self.to_dict()) def __repr__(self): """For `print` and `pprint`""" return self.to_str() def __eq__(self, other): """Returns true if both objects are equal""" if not isinstance(other, Metric): return False return self.to_dict() == other.to_dict() def __ne__(self, other): """Returns true if both objects are not equal""" if not isinstance(other, Metric): return True return self.to_dict() != other.to_dict()

25.709402

204

0.553358

699

6,016

4.672389

0.194564

0.036742

0.073484

0.288732

0.256583

0.248928

0.091855

0.06981

0.02327

0

0.015869

0.350565

6,016

233

205

25.819742

0.820067

0.301695

0

0.110092

0

0.032049

0

1

0.155963

false

0

0.045872

0

0.366972

0.018349

0

null

0

null

0

1

0

87b93feaa8699f8cd81fb2e0b56446953fa173b5

1,816

py

Python

loglog.py

nmillerns/affine_invariant_functions

0d5091d67fe8949341328b0324870b44358321bc

[ "MIT" ]

2

2020-06-03T04:30:48.000Z

2020-06-03T04:34:36.000Z

loglog.py

nmillerns/affine_invariant_functions

0d5091d67fe8949341328b0324870b44358321bc

[ "MIT" ]

null

loglog.py

nmillerns/affine_invariant_functions

0d5091d67fe8949341328b0324870b44358321bc

[ "MIT" ]

1

2020-06-03T04:30:56.000Z

import cv2 import numpy as np import sys from utils import * def logsawtooth(x: float) -> float: return (x+1)/(2**np.floor(np.log2(x+1))) - 1. class LogLogImagePattern(ImageSurface): def __init__(self, img: np.array): super().__init__(img) self.domain = SurfaceDomain(-1, -1, 15, 15, False, False, False, False) def __call__(self, x: float, y: float) -> typing.Tuple[int, int, int]: u, v = logsawtooth(x), 1. - logsawtooth(y) return self.img[int(v*(self.height-1)),int(u*(self.width-1)),:] def main(args: typing.List[str]) -> int: if len(args) != 1: print("Usage: loglog.py (imgfile.png)") return 1 plotter = ColorSurfacePlotter(900, 900, show_axis = True, axis_thickness = .02) f = LogLogImagePattern(crop_max_square_from_img(cv2.imread(args[0]))) scale = 1. translation = 0. frame = 0 while scale > 0.5: A, b = A_b_from_params(rotation_angle=0, scale=scale, b=np.array([[1],[1]]), b_scale=translation) plotter.plot_affine(f, A=A, b=b, window=SurfaceDomain(-1.99, -1.99, 7, 7, False, False, False, False)) print(frame, scale, translation) plotter.save(f'animation{frame}.png') scale -= .031250 frame += 1 while translation >= -0.5: A, b = A_b_from_params(rotation_angle=0, scale=scale, b=np.array([[1],[1]]), b_scale=translation) plotter.plot_affine(f, A=A, b=b, window=SurfaceDomain(-1.99, -1.99, 7, 7, False, False, False, False)) print(frame, scale, translation) plotter.save(f'animation{frame}.png') translation -= 0.0625 frame += 1 print("See results in animation*.png and pattern.png") plotter.save('pattern.png') return 0 if __name__ == '__main__': sys.exit(main(sys.argv[1:]))

36.32

110

0.616189

267

1,816

4.048689

0.340824

0.083256

0.055504

0.353377

0

0.050177

0.220815

1,816

49

111

37.061224

0.713781

0

0.243902

0

0.073789

0

1

0.097561

false

0

0.097561

0.02439

0.317073

0.097561

0

null

0

null

0

1

0

87ba26c6db22b72a61c66f9d345fd9b49a972171

1,042

py

Python

api_builder/configuration.py

zmiller91/aws-lambda-api-builder

86026b5c134faa33eaa1e1268e0206cb074e3285

[ "MIT" ]

null

api_builder/configuration.py

zmiller91/aws-lambda-api-builder

86026b5c134faa33eaa1e1268e0206cb074e3285

[ "MIT" ]

null

api_builder/configuration.py

zmiller91/aws-lambda-api-builder

86026b5c134faa33eaa1e1268e0206cb074e3285

[ "MIT" ]

null

import json import os import inspect import api_builder import time EXECUTABLE_NAME = 'api_builder' APPLICATION_DESCRIPTION = ''' This is a CLI to build, package, and release AWS APIs using API Gateway and Lambda. ''' ZLAB_CONF_FILE = "zlab-conf.json" STATIC_DIR = os.path.join(os.path.dirname(inspect.getfile(api_builder)), "static") _base_dir = os.getcwd() _build_dir = os.path.join(_base_dir, "build") _private_dir = os.path.join(_build_dir, "private") _deps_dir = os.path.join(_private_dir, "deps") _zip_dir = os.path.join(_private_dir, "lib") _project_name = os.path.basename(_base_dir) _cf_dir = os.path.join(_base_dir, "cloudformation") def check_bootstrap(): if not os.path.exists(ZLAB_CONF_FILE): raise ValueError("Application not bootstrapped. Run `zlab bootstrap --name {ApplicationName}` to boostrap") def get_zlab_conf(): with open(ZLAB_CONF_FILE) as conf: return json.load(conf) def write_zlab_conf(conf): f = open(os.path.join(ZLAB_CONF_FILE), 'w') f.write(json.dumps(conf)) f.close()

30.647059

116

0.738964

162

1,042

4.475309

0.41358

0.082759

0.096552

0.107586

0.118621

0

0.134357

1,042

34

117

30.647059

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0

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0

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false

0

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0

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null

0

null

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